Novel Computational Electroencephalographic Methodologies for Autism Management


Autism and epilepsy can be serious mental health problems although an early detection of these health complications can lead to proper management and control of the symptoms associated with them (Pop-Jordanova et al. 4). Over the years, autism and epilepsy have been psychological conditions that have persistently subjected families to untold frustrations. Whereas people are aware of the medical complications and the problems associated with the late detection and interventions towards the control of autism and epilepsy, the healthcare systems have been falling short of ideas on how to incorporate early detection methods and systems for proper management of these mental health complications (Pop-Jordanova et al. 4). As explained in this project, the aim of this report is to establish the manner in which Novel Computational Electroencephalographic solutions may help in autism management and prediction of epileptic seizures. This section of the report focuses on discussing how these computational disease management and control technologies work. In the end, the section proposes future work on these methodologies.

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EEG methodologies for Autism Management

Autism is a serious mental health problem that has posed grievous challenges to the health care professionals (Pop-Jordanova et al. 4). According to the intended project for the mobile epilepsy predictive system, the first aim of the proposed project was to establish Novel Computational Electroencephalographic for autism management. Based on the results established through the extant research is that Autism Spectrum Disorder (ASD) is a common problem affecting the autism patients, although there are EEG remedies for its management and control (Sichel, Fehmi, and Goldstein 62). The ASD mental health problem is a form of a complex developmental disorder and psychological disorder that manifests in children through various characteristics of impairments associated with cognitive and psychological impairments (Pop-Jordanova et al. 4). The disease also affects the social communication and interaction abilities of the infected children, and it sometimes presents characteristics such as restricted, repetitive behaviours, activities, and interests during its diagnosis (Sichel, Fehmi, and Goldstein 61). One of the modern technologies or methodologies that can help to manage autism is the Brain Music System.

The Brain Music System works with a technology that supports the management of the Autism Spectrum Disorder (ASD) using real-time sonified brain-analysis signals (Sichel, Fehmi, and Goldstein 63). The Brain Music System is a form of an EEC therapeutic remedy and a treatment “system that uses Sonified neurofeedback accurately and cost effectively to convert brainwaves into musical sound using Digital Signal Processing algorithms” (Trevisan and Jones 1). For treatment of individual patients suffering from autism that presents multiple characteristics and numerous neurological conditions, the Brain Music System can offer efficient remedies that also offer a cost-effective approach. The inexpensive and easily portable Brain Music System is a computerised neuropathic treatment solution for the autism patients across different hospital settings as the health care professionals can use it in both the external and the internal environment of the traditional clinical environments (Coben and Myers 13). The Brain Music System offers therapeutic solutions to the autism patients suffering from various mental and neurological conditions.

The aim of the intended project was to offer reliable and cost-effective remedies for management the autism forms of disorders through a portable platform. Trevisan and Jones state that the Brain Music System acts a form of a sonified system for detecting and analysing Brain Signals (5). When tested through the “pilot studies that test for the behaviour of the Beta, Alpha, and Theta sound waves, the Brain Music System works comparatively well with the high-end confined equipment that are available in the expensive clinical setups” (Trevisan and Jones 5). The technology that supports the use of the Brain Music System believes that there exists a very big association between music sensations and the cognitive functions of the brain in human beings. According to Trevisan and Jones, the Brain Music System delivers a physiological notion that “the arousing feeling of music and the appreciation of music are essentially the interaction of the musical piece with the emotional and mental status of the listener” (1).

The Relationship between the Human Brain and Neurofeedback Music

Music is normally a sensational aspect in the social life of human beings, and it often associates with mental relaxation and cognitive development in human beings. Neurofeedback music training is a strategy that helps neurologists to provide cognitive support to the autism patients through the science of brainwave biofeedback (Trevisan and Jones 3). The biofeedback science involves a situation whereby the brainwaves remain fed into the brain system through the brain cells to help attain a pleasing rate of recurrence of the brain activity. Neurological studies have insisted that the association between the brain and music therapy exists where the neurofeedback plays an imperative role in determining the communication between the brain and the musical signals. Trevisan and Jones posit, “Brainwaves are responsible for various activities and moods of an individual, and through a keen analysis and investigation, it can be possible to change a mood to another by employing neurofeedback” (3).

Studies carried out in London to analyse the impact of neurofeedback Music on the functioning of the human brain through the Cognitive Neuroscience and Behaviour Department show that brain music therapy can help in alleviating psychological problems associated with the Autism disorder (Schuyler 77). Using the brain to alleviate problems associated with autism works perfectly with the science of cognitive neuroscience as research reveals that temporary harmonisation between the isolated neural assemblies contributes positively to the achievement of higher cognitive phenomena. Scientists believe that the multiple cortical regions of the brain often become co-active when the brain is working on a cognitive task. According to Sichel, Fehmi, and Goldstein, neuroscientists have discovered that listening to music has a great impact on cognitive development as music provides a sensational feeling to the listeners and helps to arrange and coordinate the cortical brain patterns (60). Trevisan and Jones posit, “Music is important for excitation and priming of the common repertoire and orderly flow of the cortical patterns responsible for higher brain functions” (2).

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The Methodological Approach of the Brain Music System

The Brain Music System is a form of a therapeutic remedy for the autism patients as it delivers a medical approach that comes in the form of an electroencephalographic data that produces modulated MIDI. The Brain Music System resembles the Brain-Computer Interface (BCI) system that Professor Eduardo Miranda created during the early days of testing mental health complications with sound technology. The Brain Music System is a two-channel EEG methodology that uses the ordinary computational methods, which can easily remain supported by the standard modern office computers. Such characteristics make the Brain Music System more accessible, reliable and cost-effective in terms of offering therapeutic solutions to the patients suffering from the autism disorder (Sheikhani et al. 957). The Brain Music System works efficiently using the brainwaves conversion model, which specialises in providing solutions on how transforming brainwaves to music works in the analysis of the Autism disorder. The technology uses the Discrete Fourier Transform (DFT) and the Power spectrum analysis (PSA).

The initial stage of “Power Spectrum Analysis (PSA) is the stage where the analysts calculate the Fourier transform indicated as I(x, y) and the square modulus of the Fourier Model using the format to form the power spectrum with a model p (u, v)” (Trevisan and Jones 4). The general format for acquiring the power spectrum is p (u, v) = |FT [I(x, y)] |2. The format for power spectrum allows the Brain Music System to analyse various computerised images that provide significant data for assessing the brain functions. During the process of assessing the power spectrum, it is important to find the actual Power spectrum analysis (PSA) to establish the real values that can help determine the brainwaves. The process of transforming brainwaves into music signals also involves the process of determining the Discrete Fourier Transform (DFT), which is a process that allows an easy computation of the spectral rays from a discrete-time data.

According to Trevisan and Jones, Discrete Fourier Transform (DFT) is a format that believes that computation of the spectral rays from a discrete-time data is imperative in assessing brain signals because discrete-time data enables the analysts to calculate the exact spectral in the computerised images (2). Additionally, Trevisan and Jones postulate that an “accurate computation of spectral rays is significant for conversion of brainwaves to music waves so that least redundancies result” (3). While demonstrating the manner in which the brain works to create music and rhythmic activities, Trevisan and Jones note that the brain has two hemispheres that compose the main portion of the brain (5). The first hemisphere is the right hemisphere, which associates with the creativity aspect of human beings, and the second one known as the left hemisphere, which deals with the logic abilities of the human beings. The local abilities in the field of neuroscience are the brain structures and their relative functions.

The distinctive functions of the two hemispheres, the right and left hemispheres, demonstrate the manner in which the two spheres are relevant in understanding how the brain creates music or responds to music. To help understand how the two hemispheres work concurrently to support the brain to create or meditate music, Levitin developed a mental health study to analyse the functionalities of the right and the left hemispheres in music creation and music reception (16). Levitin then discovered that an activity carried out in the prefrontal cortex of the brain (a place where positive emotions such as happiness or joy exist) affected an activity in the right prefrontal cortex of the brain (a site where negative emotions and wild anxieties occur (24). Such occurrences explain how the Brain Music System works for accessing, diagnosing and managing children suffering from mental health disorders such as the autism disorder of the brain.

The Sonified Neurofeedback Therapy

The technology that supports the use of a sonified neurofeedback therapy entails the use of brainwaves to determine the intelligence and different abilities of the children suffering from the Autism Spectrum Disorder. The neurofeedback therapy provides the neuroscientists with medical remedies associated with musical signals while dealing with children suffering from Autism Spectrum Disorders. The sonified neurofeedback therapy uses four major steps namely the data acquisition phase, the data pre-processing phase, the development of visual and sonic maps, and finally, the visualisation and sonification phase. In the technology of using the sonified neurofeedback therapy, the first step that is known as the “data acquisition process entails a process of data handling that starts with the detection of the variables and ends with the process of magnetic recording and other relevant data recording processes” (Trevisan and Jones 1). The data recorded then undergoes some syntheses in the pre-processing phase of the neurofeedback therapy.

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The second important process in the technology of using the sonified neurofeedback therapy is the pre-processing phase. The data pre-processing stage of the sonified Neurofeedback therapy is a process of sorting data or pre-processing the collected raw data to get the appropriate input to represent the required data before developing the actual sonification and visual output for the neurofeedback therapy. In the process of dealing with the neurofeedback therapy, “visualisation acts as a process of interpreting the image data available and computerising the data to develop images from the available multi-dimensional data” (Fuchs et al. 6). The complicated multi-dimensional data provides the system with a platform to create visual patterns from the recorded data in the third stage of a neurofeedback therapy. After visualisation, the sonification process terminates the fourth stage by transforming the recorded data into an acoustic signal to enable the communication and interpretation of the coded data.

How the Brain Music System Works

The computerised Brain Music System reveals a form of technology that aims at boosting and equalising the brain activities in which four major brainwaves act as important elements of understanding how the human brain works. Patients suffering from autism-related disorders such as the Autism Spectrum Disorder tend to have abnormalities in the functioning of the brain whether when awake or sleeping (Fuchs et al. 6). The brain works in such a way that the four brainwaves involved in the psychological well-being of the humans must function in normalcy. The four brain waves known as Alpha (denoted with a symbol (α), Theta, Beta (denoted with a symbol β), and Delta brainwaves (denoted with a symbol δ), must work without any alterations during the brain activity. In the Neuroscience studies, the Beta and the Theta brainwaves are responsible for proper concentration and mental activity in normal human beings, while the delta brainwaves associate with mental relaxation and inactiveness.

When the brain activities of the normal healthy persons are analysed during a deep sleep period, the delta brainwaves tend to be higher than the Alpha and the Beta brainwaves (Fuchs et al. 3). This assessment reveals that when human beings are asleep, the brain tends to be inactive or dormant during the sleeping sessions. Nonetheless, the trends in the brain activity for people suffering from autism spectrum disorder are different as these patients have the tendency of having high levels of delta brainwaves throughout their lifetimes (Fuchs et al. 7). Studies carried out to compare the brain function and symmetry activity in normally developing children and in children suffering from the Autism Spectrum Disorder, reveal that ASD resulted from higher delta wave brain activity. Currently, there is no cure for patients suffering from the Autism Spectrum Disorder (Fuchs et al. 3). As one of the computerised electroencephalography (EEG) solutions, the Brain Music System provides a remedial platform to balance the brainwaves.

A discussion of the Actual Research with the Brain Music System

To determine the effectiveness of the Brain Music System as a sonified neurofeedback therapy, Trevisan and Jones conducted research for this technological model with two distinct objectives (6). The first objective of the research was to determine if there exist common patterns and levels of brainwave processes in the EEG outputs, which are essential in the musical process of the Brain Music System (Trevisan and Jones 6). The second objective of their research was to compare the levels of output brainwaves or the modified LORETA, with the prevailing literature in the neuroscience studies (Fuchs et al. 5). The two objectives primarily wanted to examine the existing relationship between the actual human brains, the Brain Music System, and its ability to generate music as one of the computational electroencephalography (EEG) systems. In testing the common patterns, the levels of brainwaves, and the presence of the output brainwaves, researchers can identify how the sonified neurofeedback therapy works in children with autism.

While trying to understand how a portable sonified neurofeedback therapy can help children with autism to communicate despite their impairments associated with the speaking abilities, it is important to consider the four sound components of the computerised Brain Music System influence the development of a communication. According to Kouijzer et al., the four components of sound that emerge vital in understanding the workability of the Brain Music System are the beta, alpha, theta, and delta sound waves that help when the brain is communicating with the body parts that make speaking possible (497). The beta, delta, theta, and alpha are brainwaves that determine the achievement of an effective communication between the brain and the sound-producing organs of the body. These four brainwave components also support the rates of brain oscillations or the rapidity of oscillation (popularly known as the brainwaves). The research recorded the behaviours of the four output components of the brainwave signals and delivered its results as follows.

The research recorded the behaviours of the four output components of the brainwave signals

The above results are recording of brain waves achieved from 10 research subjects associated with the research. The four components of the EEG system presented different recordings in the Brain Music System, and their results appeared as described below. Throughout the analysis of the brainwaves and the frequency signals, the results show that the band frequency varied between different subjects but presented similar results when put into a comparative analysis. In the research, the Beta brainwave “recorded the highest mean wave, the Theta brainwave and the Alpha brainwave then followed in a respective manner” (Trevisan and Jones 6). The least form of brainwave achieved in the analysis was the delta brainwave, which recorded lower figures in several instances among the present research subjects. The first EEG component in the computerised Brain Music System is the Beta brainwave component that gave the highest recordings. The Beta wave gives this research an “avenue for assessing the interaction between music and the brain” (Trevisan and Jones 6).

The results of the research reveal that the left frontal areas of the human brain get more involved in the processing and distribution of information across the system (Kouijzer et al. 497). Additionally, the “right hemisphere may experience higher frequencies and higher engagements across the spectrum of the Beta wave” (Trevisan and Jones 6). Therefore, the Beta wave in this case helps the neurologists to understand how a portion of the human brain, whether from the left frontal part or from the right hemisphere, helps in processing a certain kind of music or musical sensations (Kouijzer et al. 497). In a different study conducted to analyse how a portable Sonified Neurofeedback Therapy can support patients with Autism Spectrum Disorder, Trevisan, Cavallari, and Attard provided a significant portion of research that explains this association (7). In a study of 10 research subjects, the three researchers analysed the behaviour of the three main brainwaves coupled with how they help people understand the association between music and the Brain Music System.

Neuroscience studies believe that people diagnosed with Autism Spectrum Disorder (ASD) have the tendency of portraying greater levels of Delta brainwaves, and lower levels of the Beta and Alpha brainwaves. According to the findings of Trevisan et al., the Beta brainwaves are significantly high in the computerised Brain Music System, while the Theta and the Alpha brainwaves follow respectively (11). In their findings, the three researchers discovered that since children and patients suffering from autism tend to portray low levels of Beta and Theta brainwaves, which are significant elements of the brain that associate with focus and mental, a Brain Music System can help alleviate concentration problems among the ASD patients. In the above readings, the Beta brainwave tends to enhance the brain focus and mental activity tend to demonstrate how the computerised Brain Music System can reduce the levels of mental impairments and support cognitive development in the patients suffering from the Autism Spectrum Disorder.

While reviewing the behaviours of the brainwaves, Trevisan and Jones discovered that the theta band showed a coherent appearance in the 10 subjects and this coherence occurred in a symmetrical manner except in a few cases between the subjects (9). The Alpha band demonstrated a “more coherent decrease in the ten (10) subjects and stretched on longer distances compared to the other bands” (Trevisan and Jones 6). Interpretively, “increases in the coherences can be best explained by the theory that explains the increasing cooperation between the two hemispheres of the brain” (Trevisan and Jones 6). According to Trevisan and Jones, “the two hemispheres that make up the major portion of brain, is the right hemisphere which is associated with creativity and the left hemisphere which is associated with logic abilities such as creativity and mental focus” (1). On the other hand, “decreases indicate that the brain activity in the assessment requires lower levels of collaboration between the two hemispheres to perform with normalcy” (Trevisan and Jones 6).

Band frequency graph (Trevisan and Jones 6)
Band frequency graph (Trevisan and Jones 6)

The above results are similar to several pieces of literature that have dealt with the understanding how the neurologists can use a sonified neurofeedback therapy to help children with autism to balance their brainwaves through the computerised platforms. In a study to examine the impact of Electroencephalographic Biofeedback in the treatment of people suffering from attention deficiency and hyperactivity disorders, Monastra et al. examined how hyperactivity and attention deficiency disorders occur, and how the human EEG can alleviate the problems (95). These researchers discovered that attention-deficit/hyperactivity disorder (ADHD) is characterised by a situation where there is increased relative theta power, reduced alpha and beta brainwaves and increased theta/alpha and theta/beta brainwave ratios. Alterations in the frontal, mid-line and central regions of the brain highly relate to the development of the attention-deficit/hyperactivity disorder (ADHD). The research of Monastra et al. delves much on bringing an understanding of how musical signals and brain waves can work to produce Electroencephalographic Biofeedback (97).

In their research, Monastra et al. reveal that an EEG biofeedback provides significant remedies for supporting people with mental disorders as it provides a platform for understanding how the thalamocortical mechanism develops in the human brain and how it plays a role in analysing the formation of rhythms and frequency modulations in the human brain (97). Like the Brain Music System, the thalamocortical mechanisms help to form rhythms and frequency modulations that support the formation of Electroencephalographic Biofeedback that neutralises the actions of the brainwaves during the brain activity (Coben 131). In a similar study of the relationship between the science of neurofeedback training and the autism spectrum disorders, Wang et al. discovered that patients who suffer from the Autism Spectrum Disorder normally demonstrate high levels of delta brainwaves than the alpha and beta brainwaves (3). Therefore, to assess this relationship, Wang et al. decided to break down the oscillatory brain patterns into musical bands or sound frequencies that share common physiological properties in the brain activity (9).

Studies about the association of neurofeedback and better mental statuses in patients with autism have repeatedly revealed that music can form a significant part of mental healing in several cases of autism (Trevisan, Cavallari, and Attard 9). In a different study conducted to investigate the manner in which neurofeedback training influences behaviours in persons suffering from Asperger’s Syndrome, which is one of the numerous mental and neurological conditions associated with the Autism Spectrum Disorder, Thompson and Thompson unveiled some unique findings (64). The two researchers discovered that patients who suffer from the Asperger’s Syndrome have low-frequency beta activity and high-frequency delta activity in the functions of their brains (Thompson and Thompson 64). According to Coben, the fluctuations in the behaviours of the beta and the delta brainwaves determine the mental status of people, regardless of whether they are normal or have some abnormalities (135).

EEG methodologies for Epileptic Seizure Prediction

Epilepsy is another mind-nabbing mental health problem that has caused psychological distresses in many modern families across the world. Epilepsy is still a challenge to the health care professionals and the healthcare fraternity due to its complex nature in the brain systems. In the field of medical technology, the role of the human electroencephalogram (EEG) continues to be imperative in the prediction, detection and management of the epilepsy conditions in human beings. More importantly, the human electroencephalogram (EEG) technology is becoming increasingly important in the early detection and prediction of the epilepsy condition in people suffering from the epileptic seizures (Coben and Myers 15). Based on the scientific evidences developed concerning the relevance of the electroencephalograph technologies in determining the prediction and detection of seizures in epileptic patients, neuroscientists have discovered that an electroencephalograph is capable of recording the spontaneous electrical activities that occur in the cerebral cortex.

Although epilepsy has a long history, Epilepsy practitioners have historically established that many patients who suffer from the epilepsy disorder understand that their seizures do not occur abruptly (Coben and Myers 17). Most epileptic patients understand that their seizures do not occur abruptly in onset, but they do occur in predictable periods throughout their lifetimes. Portable electroencephalographic technologies are currently the most advanced in the field of neuroscience across the world. In dealing with epileptic conditions associated with the children and the youngsters, the portable electroencephalographic technologies play a significant role in determining the levels of seizure focus, accessing the nature of epileptic attacks, and determining the levels at which the epileptic attacks occur in the different scientific findings. According to Karimi, Haghshenasb, and Rostamic repeated studies show that the use of electroencephalographic technologies has significant impact on the prediction and detection of the seizures as this technology deals directly with the brain activity and the various brain functions (1472).

The predictable nature of the onset of epileptic seizures makes the use of portable electroencephalographic technologies a relevant concept in the field of Neuroscience. Epileptic seizures are periodic in nature, as scientists have discovered that changes in the blood flow of the epileptic patients occur 12 minutes even before the seizures begin. Clinical prodromes of such occurrences have revealed in at least more than fifty percent of the epileptic patients tested for the study about the occurrence of seizures (Ozdemir and Esen 8). Further studies on the occurrence of the epileptic seizures through the technology of the Magnetic Resonance Imaging (MRI) show that before the occurrence of seizures, signals of blood oxygen levels have been reported (Karimi, Haghshenasb, and Rostamic 1474). Therefore, with the use of human electroencephalographic, biofeedback can be imperative in predicting, detecting, and managing epileptic seizures.

How the EEG helps in Epilepsy Detection and Management

As an effective methodology for detecting and managing the epileptic seizures, the human electroencephalogram (EEG) has several ways in which it supports in the prediction, detection, and management of the epileptic patients who demonstrate seizure characteristics (Moghim and Corne 9). The human electroencephalogram (EEG) technology helps the Neuroscientists to find efficient ways of diagnosing the epilepsy disorder and managing the epilepsy disorder through a series of medical strategies (Moghim and Corne 9). According to Tripathi and Mehendiratta, the human electroencephalogram is a technology that helps in the diagnosis of the epilepsy disorder through various processes (16). These processes include the diagnoses carried out in neurological paroxysmal events, the distinction between the generalised and the focal seizure disorders, investigation of the specific syndrome changes and the recognition of photosensitivity problems in epilepsies (Binder 249). A portable or mobile epilepsy predictive system can offer significant assistance in the diagnosis of the epilepsies.

One of the mentioned diagnosis procedures for epilepsy is the differential diagnosis that often takes the science of analysing the paroxysmal neurological events (Moghim and Corne 10). In a study of the human electroencephalogram (EEG) and its influence on the diagnosis of the epileptic seizures experienced by the epileptic patients, Tripathi and Mehendiratta sought to investigate the performance of electroencephalogram (EEG) equipment known as the Video EEG monitoring (23). According to the research of Tripathi and Mehendiratta, the diagnosis of the epileptic seizures is very intricate and the values sometimes yield poor results due to the inability of some EEGs to detect and measure the actual events or attacks (21). This assertion holds because confirming seizures on actual attacks or events sometimes becomes a rare achievement during the standard 20-30 minutes of recording the brainwaves and the frequencies (Binder 250). The diverse role of the VEEG technology spans from the period of diagnosis to the period of epilepsy management.

The Visual EEG monitoring technology is an appropriate tool for measuring the epilepsy seizures as it can operate during the normal inpatient treatment hours, around the workplaces or home environments and even with the use of an ambulatory EEG (Drury 11). The Visual EEG monitoring technology provides several important mechanisms through which the EEG technology works during the assessment of the epileptic seizures in the patients suffering from epilepsy (Drury 11). The Visual EEG can allow for the confirmation of the various types of epilepsy attacks and the none-epileptic occasions such as the paroxysmal movement disorders, the pseudo-seizure problems, and the sleep disorders. The VEEG technology also supports the determination of seizure attention in epileptic patients with nonconforming features such as the frontal lobe seizures and the gelastic seizures that probably require pre-surgical assessments (Tripathi, Yadav, and Kumar 276). Further analysis shows that the VEEG technology supports the exact classification of seizures prior to appropriate therapies.

The Actual use of the Portable EEG technologies

The proposed project on the development of the mobile epilepsy predictive system focussed on the use of the EEG technologies in assessing the onset of an epileptic seizure (Drury 14). The EEG technology is still the central tool for the diagnosis and management of the epilepsy patients due to its ability to provide cheap remedies and convenient procedures for analysing the abnormal cortical excitability that associates with the epilepsy disorder (Tripathi, Yadav, and Kumar 276). The technology of measuring the seizures is the same as the one used in the management of autism patients through the Brain Music System, as the technology of assessing the behaviours of the brainwaves also applies to the detection and prediction of the occurrence of seizures (Tripathi, Yadav, and Kumar 276). The EEG technology relies on the use of the beta, theta, delta and the alpha brainwave technology to predict the possibility of having seizures in patients suffering the epilepsy psychological disorder (Tripathi, Yadav, and Kumar 276). Extant studies have shown how this technology works.

In a research of the functions of the Video EEG monitoring, Tripathi and Mehendiratta discovered that the beta, the alpha, the theta, and the delta brain waves or brain signals have an important role in developing an understanding of how the EEG technology predicts and detects the epileptic seizures (22). Tripathi and Mehendiratta add that the four brainwaves provide the neurologists with an opportunity to access the brain activity through a system known as a spike-wave pattern (23). Nonetheless, the behaviours and occurrences of the brainwaves in the EE technology rely on the type of seizures involved and the nature of the seizure when viewed from the neurological paradigms. While dealing with the temporal lobe seizures, the alpha and the theta brainwave play a significant role in the prediction and detection of the seizures in the epileptic patients. Alessandro et al. conducted research to examine the behaviours of electromagnetic seizures (603).

In their study, Alessandro et al. employed an intelligent genetic search procedure to analyse the multiple contacts of intracranial electrodes and the several quantitative characteristics obtained from the electromagnetic signals (603). The method involved the collection of electromagnetic data from multiday recordings achieved from the four selectively targeted patients with intracranial electrodes planted on their heads while they were undergoing assessment for epilepsy surgeries. The study came up with an estimated 6.5% record in seizure block sensitivity and an average block false positive of 0.2775 False Positive (FP) predictions. What appeared real in this electromagnetic brain study, just like in other studies, was the behaviour of the brainwaves that provided space for the calculation of signals and frequencies. There was a presence of the theta brainwaves in this analysis. According to this research, Alessandro et al. discovered that the theta brainwaves play an important role in the assessment of the epileptic seizures (606). See the diagram below.

Analysis of the theta brainwaves

The prediction and detection of the epileptic seizures rely on the detection of the brainwaves such as the theta brain signal was imperative in determining the behaviours associated with the short and the long temporal spiking during the occurrence of the epileptic seizures in the epileptic patients. Using four categories of patients subjected to an electrode test where some intracranial electrodes were implanted in their heads, the research discovered that the theta rhythmic signals are essential in determining the longevity of the epileptic seizures. Just as Tripathi and Mehendiratta noted in their research, “most often the initial frequency of temporal lobe seizures is in the alpha or theta range with slower frequencies occurring in a lesser proportion” (20). In an empirical research on the four patients named as Patient A, Patient B, Patient C, and Patient D, during the assessment of the electromagnetic signals in detecting epileptic seizures, the theta brainwaves played a significant role.

In their analysis, Alessandro et al. discovered that the theta signals or the rhythmic theta were responsible for the occurrence of the long epileptic periods that involved stopping of the left temporal spiking and an involvement of a suppression within the EEG background (612). In the same instance, some theta brainwaves had amplitude of 4-7.5Hz that prevailed for about 5-second durations in each of the four patients placed on the electromagnetic implants. The beta brainwaves are also part of the EEG technology especially in instances where the technology is trying to estimate the occurrence or predict the prevalence of the epileptic seizures in the epileptic patients. The research of Alessandro et al. revealed that the beta brainwaves are part of the brain activity during the occurrence of the epilepsy seizures (612). According to Alessandro et al., the four patients demonstrated the presence of the beta brainwaves in the assessment of the occurrence of the epileptic seizures (611).

Alessandro et al. noted that in most cases of the electrode activity, most seizures remain accompanied by some few seconds of the focal beta activity that occurs in the posterior electrodes on the right hemisphere of the brain (RT4-6) then a rhythmic activity spreads instantly through the various regions of the brain (612). Alessandro et al. posit that the rhythmic activity across the inferior frontal, the right temporal and the inferior temporal areas during the time at which the EEC was recording (612). The delta brainwaves are also part of the EEG prediction and detection system for the occurrence of the epileptic seizures in the epileptic patients. According to their findings, Alessandro et al. also discovered that most of the patient seizures originated from the right hemisphere of the brain and in the areas where there were posterior contacts with the brain (612).

A Comparison of the two Methods for Autism and Epilepsy

A significant factor that stands out in the two systems of solving mental health complications is the use of the neurofeedback strategies in assessing the brain activity coupled with how it coordinates its rhythmic waves in relation to the music technology (Lipton 929). According to the presented research, neurofeedback training can form an imperative solution to the children suffering from autism as it provides autistic spectrum solutions to the patients (Dzhala 7876). Since the delta brainwave seems to be the foremost aspect in the behaviours of children suffering from autism, using neurofeedback training solutions can help the affected children to balance their brainwaves and communicate through the Brain Music System (Lipton 930). The two technologies for providing solutions to the autism and epileptic patients also involve the use of the electromagnetic technology, where the electrodes play a vital role in assessing the behaviours of the brainwaves in the experiments.

The scientific science that seems to dominate in the two systems of providing solutions to the autism and the epileptic patients is the science of human electroencephalogram (EEG). While the neurofeedback technique in the Brain Music System provides an approach whereby the neuroscientists analyse the behaviour of the brainwaves from a dire perspective, the portable epilepsy-detecting device uses an indirect approach to the assessment of the brainwaves (Hughes and Bromfield 201). According to researchers, the two technological components of the neuroscientists use an assessment of the behaviours of the brainwaves to detect changes in the behaviours of the individuals suffering from either the autism disorder or the epileptic seizures. Four major brainwaves characterised the development of the two EEG technologies for the autism patients and the epileptic patients (Lipton 928). In the issue of autism spectrum disorder, children suffering from this kind of a disorder tend to have more delta brainwave and low alpha and beta brainwaves.

The human electroencephalogram (EEG) technology for developing the mobile epilepsy predictive system has similar traits as the human electroencephalogram (EEG) used in developing the portable Radio Music System that would help children suffering from autism (Hughes and Bromfield 202). The two technologies have persistently used the beta, alpha, theta, and delta brainwaves to demonstrate how the brain activity operates and how the EEGs can help to reduce the instances of epilepsy occurrences and the autism impairments. Both the portable Radio Music System and the portable epilepsy predictive system rely on the beta, delta, alpha and theta brainwaves to explain the behaviours of the brain activity across the right temporal regions of the brain, the inferior frontal brain areas and the inferior temporal brain areas (Hughes and Bromfield 203). The two EEGs demonstrate how the brain or the neural signals associate with music, sound waves, and brain sound waves interrelate. They highly recognise the contribution of brainwaves with the functioning of the systems.


Autism Spectrum Disorder and epilepsy continue to be among the serious mental health problems that are constantly causing psychological problems and distresses to the victims. Autism is among the world’s most irritating neurological and developmental disorders due to the psychological impact it has on many children attacked by its various complications. Nonetheless, since neurologists have fallen short of the required medical health solutions to offer complete treatments to patients suffering from autism and epilepsy due to the complex nature of these neurological and developmental disorders, modern technology has come up with some effective remedies. The proposed project of designing portable neurofeedback training solutions and mobile epilepsy predictive system specifically relies on the human electroencephalogram (EEG). The human electroencephalogram (EEG) is an effective technology that helps the neurologists to offer therapeutically driven solutions to the patients who portray symptoms of autism and those who demonstrate seizure disorders. Given the fact that the clinical manifestations of the two mental health disorders are complex to control using the ordinary treatment options, neurofeedback solutions are very effective.

Future Work on Portable Epileptic and Autistic EEGs

Future work on the severity of autism and other mental health issues

While trying to understand the work of the human electroencephalogram (EEG) technology and the Brain Music System in managing the conditions of children and patients with autism through the balancing of brainwaves, it is important to note that autism has different levels of severities (Haut 276). In the future, neurologists should attempt to investigate the likelihood of using the neurofeedback training options and the human electroencephalogram (EEG) technology in dealing with patients suffering from chronic autism and other severe impairments related to the brain such as lower brain functioning for the psychologically-upright individuals.

The nature of autism and epilepsy in human beings

Autism and Epilepsy are neurological and developmental disorders that normally begin during the teenage stages of the children before they fully manifest in patients at their mature stages of life. Excessive promotion of the use of the human electroencephalogram (EEG) technology may result in circumstances whereby the ordinary people can no longer predict even the simplest symptoms of autism and epilepsy (Schulze-Bonhage, Feldwisch-Drentrup, and Ihle 89). Therefore, future investigations should concentrate on developing and understanding how the human electroencephalogram (EEG) technology will affect the ability of the ordinary people to continue detecting autism and epilepsy symptoms.

The neurofeedback training and other interventions

Since medical technologies are meant to provide reliable solutions and help in improving the nature of treatment options and the related outcomes, future clinical research should establish the connection between the uses of human electroencephalogram (EEG) technology and other medical interventions (Scaramelli and Braga 247). For instance, research should focus on establishing how the human electroencephalogram (EEG) technology can work with other mental health interventions to analyse the synergistic effects that exist between other remedies and the neurofeedback training intervention (Scaramelli and Braga 247). Other combinable interventions that can work with the human electroencephalogram (EEG) technology may include the hyperbaric oxygen therapy (HBOT) and the behaviour therapy.

Efforts to optimise seizure prediction

The current technological strategies meant to develop solutions for the autism and epilepsy patients are lacking the ultimate solutions for deterring the occurrence of seizures in patients (Fricker 562). The mainstream EEG technology has only offered solutions on the detection and prediction, and slightly on the controlling of the seizures, but has failed to offer options to deter or abort the seizures in the epileptic patients. According to Fricker, this aspect means that the antiepileptic devices developed through the human electroencephalogram (EEG) technology are only offering solutions that even the ordinary people can detect using physical assessment skills (559).

Seizure prediction and the future of the epileptic patients

Future investigations and research should focus on establishing how the human electroencephalogram (EEG) will affect the human beings and how the same technology can be useful in providing permanent solutions to the prevention of the occurrence of seizures (Alotaiby and Saleh 17). The future Neuroscience research should focus on analysing how the human electroencephalogram (EEG) can use permanent technologies such as the implantable devices to predict the occurrence of seizures in the epileptic patients (Alotaiby and Saleh 17). The future research should also focus on establishing how these implantable devices or technologies will affect the overall brain activity of the patients suffering from epilepsy.

Clarity of some neurofeedback training techniques

Future research on the use of neurofeedback training technique on the autism patients should focus on several issues. One is explaining the prevailing relationships between the concepts of the rostral and dorsal ACC, and the involvement of the two concepts and the posterior cingulated cortices in the use of the neurofeedback treatment as a remedy used to treat patients suffering from autism (Schulze-Bonhage, Feldwisch-Drentrup, and Ihle 89). This assertion holds because the use of the neurofeedback training technique seems to have several complex strategies that are either confusing in nature or made up of mere exaggerations just to endorse the use of neurofeedback treatment in autistic patients.


Alessandro, Maryann, Rosana Esteller, George Vachtsevanos, Arthur Hinson, Javier Echauz, and Brian Litt. “Epileptic Seizure Prediction Using Hybrid Feature selection over Multiple Intracranial EEG Electrode Contacts: A Report of Four Patients.” IEEE Transactions on Biomedical Engineering 50.5 (2003): 603-615. Print.

Alotaiby, Turkey, and Alshebeilic Saleh. “EEG Seizure Detection and Prediction Algorithms: A Survey.” EURASIP Journal on Advances in Signal processing 13.1 (2014): 1-21. Print.

Binder, Devin. “Toward new paradigms of seizure detection.” Epilepsy Behaviour 26.3 (2013): 247–252. Print.

Coben, Robert. “Connectivity-Guided Neurofeedback for Autistic Spectrum Disorder.” Biofeedback 35.4 (2009): 131–135. Print.

Coben, Robert, and Thomas Myers. “The Relative Efficacy of Connectivity Guided and Symptom Based EEG Biofeedback for Autistic Disorders.” Applied Psychophysiology Biofeedback 23.2 (2010): 13–23. Print.

Drury, Smith. “Seizure prediction using scalp electroencephalogram.” Experimental Neurology 184.1 (2003): 9–18. Print.

Dzhala, Staley. “Transition from interictalictal activity in limbic networks in vitro.” Journal of Neuroscience 23.1 (2003): 7873–7880. Print.

Fricker, Miles. “EPSP amplification and the precision of spike timing in hippocampal neurons.” Neurology 28.19 (2000): 559–569. Print.

Fuchs, Niels, Birbaumer Lutzenberger, John Gruzeiler, and Jochen Kaiser. “Neurofeedback Treatment for Attention-Deficit/ Hyperactivity Disorder in Children: A Comparison with Methylphenidate.” Applied Psychophysiology and Biofeedback 28.1(2003): 1-10. Print.

Haut, Sheryl. “Predicting Seizures: Are We There Yet?” Epilepsy Currents, 13.6 (2013): 276–278. Print.

Hughes, Devinsky, and Feldmann Bromfield. “Premonitory symptoms in epilepsy.” Seizure 2.1 (1993): 201–203. Print.

Karimi, Masoumeh, Sajjad Haghshenasb, and Reza Rostamic. “Neurofeedback and autism spectrum: A case study.” Social and Behavioural Sciences 30.1 (2011) 1472-1475. Print.

Kouijzer, Mirjam, Kouijzer Jan Moor, Berrie Gerrits, Jan Buitelaar, and Hein Van-Schie. “Long-term effects of Neurofeedback treatment in autism.” Research in Autism Spectrum Disorders 3.1(2009): 496–501. Print.

Levitin, Daniel. “Neural Correlates of Musical Behaviours: A Brief Overview.” Music Therapy Perspectives 31.1(2013): 15-24. Print.

Lipton, Bruce. “Predicting seizures: A behavioural approach.” Neurological Clinics 27.1 (2009): 925–940. Print.

Moghim, Negin, and David Corne. “Predicting Epileptic Seizures in Advance.” PLOS ONE 9.4(2014): 1-17. Print.

Monastra, Vincent, Steven Lynn, Michael Linden, Joel Lubar, John Gruzeiler, and Theodore LaVaque. “Electroencephalographic Biofeedback in the Treatment of Attention-Deficit/Hyperactivity Disorder.” Applied Psychophysiology and Biofeedback 30.2 (2005): 95-114. Print.

Ozdemir, Nilufer, and Yildirim Esen. “Patient Specific Seizure Prediction System Using Hilbert Spectrum and Bayesian Networks Classifiers.” Computational and Mathematical Methods in Medicine 3.1 (2014): 1-10. Print.

Pop-Jordanova, Nada, Tatjana Zorcec, Aneta Demerdzieva, and Zoran Gucev. “QEEG characteristics and spectrum weighted frequency for children diagnosed as autistic spectrum disorder.” Nonlinear Biomedical Physics 4.4 (2010): 1-7. Print.

Scaramelli, Alejandro, and Patricia Braga. “Prodromal symptoms in epileptic patients: Clinical characterisation of the pre-ictal phase.” European Journal of Epilepsy 18.4 (2009): 246–250. Print.

Schulze-Bonhage, Andreas, Hinnerk Feldwisch-Drentrup, and Mathias Ihle. “The role of high-quality EEG databases in the improvement and assessment of seizure prediction methods.” Epilepsy Behaviours 22.1 (2011): 88–93. Print.

Schuyler, White. “Epileptic seizure detection.” IEEE Engineering in Medicine and Biology 26.2 (2007): 74–81. Print.

Sheikhani, Ali, Hamid Behnam, Mohammad Mohammadi, Maryam Noroozian, and Mohammad Mohammadi. “Detection of Abnormalities for Diagnosing of Children with Autism Disorders Using of Quantitative Electroencephalography Analysis.” Journal of Medical Systems 36.2 (2010): 957–963. Print.

Sichel, Arthur, Lester Fehmi, and David Goldstein. “Positive Outcome with Neuro- feedback Treatment in a Case of Mild Autism.” Neurofeedback and Applied Neuroscience 1.1 (2008): 60-64. Print.

Thompson, Lynda, and Michael Thompson. “Neuro-feedback Outcomes in Clients with Asperger’s Syndrome.” Applied Psychophysiology Biofeedback 81.1(2010): 35:63. Print.

Trevisan, Adrian, and Lewis Jones. “Brain Music System: Standardised Brain Music Therapy.” Journal of Neurological Disorders 1.2 (2013): 1-12. Print.

Trevisan, Adrian, Paolo Cavallari, and Frederick Attard. “A Portable Sonified Neurofeedback Therapy for Autism Spectrum Disorder Patients-An Initial Evaluation.” Journal of Neurological Disorders 1.4 (2013): 1-14. Print.

Trevisan, Adrian, Paolo Cavallari, Nicole Caruana, and Rita Micallef. “A Home-Based, EEG-Neurofeedback Device for the Management of Children on the Autism Spectrum.” Journal of Neurological Disorders 2.1 (2009): 2-15. Print.

Tripathi, Manjari, and Mohan Mehendiratta. “Role of EEG in Epilepsy.” Journal of Open Science 3.1(2014): 15-27. Print.

Tripathi, Manjari, Srivastava Yadav, and Rajul Kumar. “Evaluation of EEG in Child Guidance Clinic.” Delhi Psychiatry Journal 14.2(2011): 275-278. Print.

Wang, Jun, Jamie Barstein, Lauren Ethridge, Matthew Mosconi, Yukari Takarae, and John Sweeney. “Resting state EEG abnormalities in autism spectrum disorders.” Journal of Neuro-developmental Disorders 5.24 (2013): 1-14. Print.

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