Keynote Speaker

Clinical Neurofeedback

Prof Dato Dr Susie CM See has a private practice at a clinic at the Loh Guan Lye Specialist Centre in Penang. She is also a volunteer principal at the Lion's REACh Autism Centre.

She provides counselling services and has incorporated neurofeedback and HRV biofeedback into her practice. In this 3rd Asia Pacific Neurofeedback/Biofeedback Conference, she is going to present on clinical neurofeedback.

We all know that many psychological/mental disorders have significant basis in neurobiological dysfunction, thus the most common approach is pharmacological treatment, which may expose individuals to side effects.

In recent decades, with advances in brain science, the cause of abnormal brain function and mental disorders has been attributed to the low or high activity of the anterior brain lobe that presents itself in different types of psychological expressions. Hence, neurofeedback has been promoted as an alternative approach or treatment for many disorders.

Neurofeedback is a specialty field within biofeedback, which devotes itself to training control over electro-chemical processes in the human brain with specific protocol to improve brainwave activity and consequently over brain mental states.

To date, many studies have been conducted on neurofeedback therapy and its effectiveness in the treatment of many disorders. It is a safe and non-invasive procedure that can be used in clinics as a treatment approach.

The presentation covers the relevance of this therapy, decisions on protocols, as well as the practice leading to effectiveness and efficacy. Clinical procedure and management will also be discussed.

Keynote Speaker

Functional And Effective Brain Connectivity For Neurofeedback Training

Dr. Nidal is a biomedical engineer and has been doing research in brain imaging using EEG, fMRI and Opto-imaging methods. Over the years, he has accumulated a extensive knowledge base in brain imaging relating to performance and disorders.

Numerous studies have reported that psychiatric disorders are related to abnormal brain networks (Broyd et al. 2009; Stam 2014; Fornito et al. 2015). Studies also reported that some cognitive functions are associated with brain networks (He et al. 2007; Kelly et al. 2008; Barch et al. 2013; Thompson et al. 2013; Liu et al. 2015). This indicates that using connectivity Neurofeedback is a promising approach to therapeutic intervention for psychiatric disorders and to improve cognitive function.

To our understanding, there are few mainstream sets of studies on connectivity Neurofeedback and all are using real-time fMRI. One uses dynamic causal modelling to modulate the state of a functional network and cognitive performance (Koush et al. 2013, 2017). The other uses Pearson's correlation coefficients of activity time courses between 2 regions (Megumi et al. 2015). Megumi et al. (2015) identified changes in global networks caused by connectivity Neurofeedback. Recently, Ayumu Yamashita et al. (1 October 2017), proved that connectivity Neurofeedback can induce the aimed direction of change in functional connectivity, and the differential change in cognitive performance according to the direction of change in connectivity.

In his presentation the functional connectivity using EEG signals will be explained and proposed for connectivity Neurofeedback training. The various forms of functional connectivity represented in time and frequency domains, in addition to their advantages and disadvantages, will be outlined. Due to the bidirectional effect of functional connectivity, the effective connectivity will be proposed as an optimum connectivity Neurofeedback training tool. The effective connectivity will be briefly introduced and compared with functional connectivity in the case of driver cognitive distraction.

Outcome Based Neurofeedback In Pervasive Developmental Disorders (PDD)

Dr. Wahedi is Professor of Pediatrics, senior lecturer and a practicing pediatrician (MD) at the Medical College for Women and Hospital in Dhaka, Bangladesh. He is a certified neurofeedback therapist (SBCIA). He started the first neurofeedback clinic in Dhaka in 2012.

For this conference, he will present on the use of neurofeedback in children with PDD. He will present a case series study of 4 selected children diagnosed as Autism, done in Bangladesh. The Children were diagnosed as Autism and were receiving some other treatment. All three children were classified as Autism with DSM IV.

After selection of the cases ATEC translated in Bengali was administered and their symptoms were classified with incorporation of parent's interview and analysis of their medical records of diagnosis. Major area of deficiency and disorder were specified. l Speech /Language/Communication, Sociability, Sensory/Cognitive Awareness, Health/Physical/Behavior l were specified in the light of ATEC. Protocol was selected on the outcome to be achieved with aim to inhibit theta and reward Low beta and alpha.

A total 30 sessions were given and was again assessed by ATEC and parents. Daily Diary of mothers and diary of therapists were also taken in consideration.

Simultaneous EEG-fMRI For Cognitive Processes And Neurofeedback

Dr. Aamir is a biomedical engineer and has been doing research in brain imaging using EEG, fMRI and Opto-imaging methods. Over the years, he has accumulated a extensive knowledge base in brain imaging relating to performance and disorders.

Electroencephalography (EEG) and functional MRI (fMRI) are considered as important modalities for studying the brain functions non-invasively. Both modalities can measure the neural activity at different spatial and temporal resolution scale. EEG has very high temporal resolution and poor spatial resolution. In contrast, fMRI has very high spatial resolution and poor temporal resolution. Therefore, both EEG and fMRI proved as complementary neuroimaging modalities in terms of their resolution. High spatial and high temporal resolution of the brain at the same time is possible with simultaneous EEG-fMRI data acquisition. This joint data recording leads the researchers towards a multitude of problems related to the data acquisition and joint analysis of the brain activity pattern data. In this conference, Mr Rana will focus on sharing his project on simultaneous EEG-fMRI data acquisition during the cognitive processes as well as data fusion. Simultaneous EEG-fMRI neurofeedback has a great potential for NFB application due to its ability for providing better spatial and temporal resolution at the same time. However, it may involve many technical challenges in terms of data acquisition and data processing. The first NFB experiment with simultaneous EEG-fMRI was performed at Laureate Institute for Brian research. Further potential applications of real time EEG-fMRI-NFB in the development of novel cognitive neuroscience research paradigms and enhanced cognitive therapeutic approaches for major neuropsychiatric disorders, particularly depression.

Integrative Approach of Neurofeedback And Counselling For Selective Mutism: A Case Study

Kelli is a neurofeedback therapist at Pebbles in Loh Guan Lye Specialist Centre, Penang, Malaysia.

In this conference, she will present a case study of a four years old girl who refused to speak outside her home.

The psychologist diagnosed her with selective mutism and refered her for neurofeedback to calm her anxiety and also work on her socialization.

According to the Diagnostic and Statistical Manual of Mental Illness (Fifth Edition), selective mutism is defined as difficulty to speak in social situation where speaking is required. Protocol for neurofeedback training included T6-O2 Beta, F7-F8 Delta and T3-T4 Delta . After the intervention, the girl started to speak in the class and playground; have brief conversation with peers that reported from school teachers and care givers.

Content Optimization For Neurofeedback Based Treatment Of Stress And Depression

Dr. Ali is an IEEE Senior Member. He is the PI for several Funded Research Projects. In this conference, he is going to present his research on his findings on the optimal mode or content to use in neurofeedback treatment of stress and depression.

People of different age, sex, status and job conditions are suffering from different mental states like stress, depression, memory loss etc. and disorders like epilepsy, autism, etc. These people are part of our society and we cannot neglect their presence. Contrary to conventional medicinal treatment, that may cause injurious side effects, a noninvasive approach employing neurofeedback is used recently by the clinicians and psychiatrists for diagnosis and treatment. Neurofeedback treatment has shown promising results for the treatment of such conditions. There are different modes and contents used for neurofeedback like music, videos, images, games, reading, words counting, addition, the color of words, etc.

Clinicians are still exploring the best ways to employ neurofeedback. However, it is still premature in the method that can decide which mode or content will result in better treatment. It is also reported that due to non-efficacy of the treatment the subjects have withdrawn treatment, resulting in loss of time, cost and most importantly the impact could be drastic especially for depression patients. This research focuses on investigating the right mode and content for neurofeedback based treatment. The investigation will be based on selecting the optimal content to be used during neurofeedback treatment depending upon subject history, profile, personality, mental state, etc.

The expected outcome of this investigation is a systematic approach that will assist in choosing the optimal content that will ensure treatment efficacy right from the early stages. This research will make sure that the patients will continue having treatment and societal burden is eventually reduced.

The Differences Of Brainwave Pattern And Attention Among Visual Learners And Auditory Learners Using EEG

Dr. Sophian is a lecturer and researcher at the University of Malaysia, Sarawak. He is a certified neurofeedback therapist. He has introduced neurofeedback intervention to many autistic and special needs centres in Sabah, Sarawak and Indonesia.

A number of studies have demonstrated the positive effects of Neurofeedback Training on treatment of psychology disorders. In this conference, Dr Sophian is going to share about his project which studies the differences of brainwave pattern and attention among visual learners and auditory learners using EEG.

This research is an experimental research that used the pre-test and post-test research design. The participant for this project is undergraduate students with CGPA below 3.00. This project was conducted for in 3 weeks with 6 sessions for each participant. The instruments used for this experiment are the 'Brain Trainer' and T.O.V.A.

The protocol to train the participants was Fp1-Fp2 Beta which have the frequency range of 12-15 Hz. The neurofeedback training has been successful to enhance the beta activity for all of participants but still some of respondent did not improve their attention based on the TOVA scores.

Detection Of Situational Interest Through EEG For Improvement Of Classroom Teaching And Learning

Ibrahima Faye is currently an Associate Professor at Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia. In this conference, he is going to share with you his findings on the detection of situational interest through EEG for improvement of classroom teaching and learning.

Understanding how the brain works and acts during a learning process is highly useful for the development of educational tools and methods. Situational interest is one of the two types of interest that is according to psychologists affected by situation and time and is under the control of academicians.

It has been much studied in Psychology, and is proven to be an essential element for educational success and goal achievements. However most studies on situational interest are based on subjective assessments and do not consider a classroom setting. In this work, he is investigating an approach to detect unique EEG brain patterns of situational interest while resembling classroom environment.

Pre and post Study Interest (SI) test and academic test are undertaken to evaluate subjectively student’s interest. Objective and subjective data are analysed and statistically tested to obtain significant features of situational interest that will then be fed into a classifier. The finding of such EEG biomarkers will be useful for future development of feedback system for improvement of teaching/learning.

Such system could be utilized by academicians, teachers to develop educational outcome by raising student's interest.

RICD Neurofeedback Center In Chiangmai

Dr Panu is a staff psychiatrist at the Chiangmai Psychiatric Hospital and is overseeing the Neurofeedback center that has commenced at Rajanagarindra Institute of Child Development (RICD) since 1st October 2016.

In the center, there are 2 treatment modalities, Electroencephalogram (EEG) & Hemoencephalogram (HEG) Neurofeedback treatment. There are 1 psychiatrist, 3 EEG Neurofeedback therapists and 2 HEG Neurofeedback therapists working there. There were 58 patients and 460 interventions which occurred at the clinic. The patients were predominant male (80%) and had their age between 8-60 years. The most common diagnosis were autistic spectrum disorder (62%), attention deficit hyperactive disorder (14 % ) ,learning disability disorder (12 %) and anxiety and depression (12%). The patients choose neurofeedback intervention electively and were reviewed diagnosis with a psychiatrist before cluster of interventions. The outcome is excellent.

In this conference, he will be presenting interesting case reports of people with autistic disorder and mixed anxiety depressive disorder who have benefited from the neurofeedback intervention.

Clinical Hypnosis And Neurofeedback Intervention For Autonomic Dysfunction

Hiro Koo is a certified Clinical Hypnotherapist and certified Neurofeedback Therapist (SBCIA). In addition, he has a master's degree in Psychology.

He started practicing neurofeedback in 2013. He has combined neurofeedback with his hypnotherapy and pioneered Neuro-hypnotherapy. The combination of neurofeedback and hypnotherapy increases the efficacy of his treatment.

For this conference, he will discuss his approach of combining clinical hypnosis and neurofeedback for the intervention of Autonomic dysfunction.

Training Protocol For Neurofeedback Training Among Mild Cognitively Impaired (MCI) Elderly

Dr. Norsiah has been doing research in Neurofeedback and QEEG at the UNIMAS since 2010. She is a certified neurofeedback therapist (SBCIA). She has analysed the EEG of various disorders and applied neurofeedback intervention.

In this conference, she is goining to present her study aimed to report the effect of neurofeedback to enhance the cognitive performance in elderly with Mild Cognitive Impairment (MCI) by Using Quantitative Electroencelphalogram (QEEG) to record and analyzed the brain rhythms patterns of elderly before and after neurofeedback,

this study then focused on alpha wave in the neurofeedback training as it was positively associated with cognitive performance decline in elderly. With 15 sessions of alpha neurofeedback, increase in alpha absolute power was rewarded while simultaneous suppression of theta and beta2 were done in experimental group. Results showed increase of alpha absolute power while mixed result was recorded for suppression of theta

and high beta either at individual, inter and intra group level. Increase in absolute power in pre and post QEEG in inter (within group) and intra (between group) group level were significant in alpha.

Is Alpha Wave Neurofeedback Effective In Depression?

Alex is a practicing Neuro/Clinical psychologist. He is also a certified neurofeedback therapist and has been practicing neurofeedback therapy since 2008.

Alex works with a wide clientele base, including children and adults. For this conference, he will discuss on the effectiveness of Alpha wave neurofeedback in depression.

Frontal asymmetric activation has been proposed to be the underlying mechanism for depression. Recent advances in imaging technology and in the understanding of neural circuits relevant to emotion, motivation,

and depression have boosted interest and experimental work in neuromodulation for affective disorders. Real-time functional magnetic resonance imaging (fMRI) can be used to train patients in the self regulation of these circuits,

and thus complement existing neurofeedback technologies based on electroencephalography (EEG). Many case studies have reported that the enhancement of a relative right frontal alpha activity by an asymmetry neurofeedback training leads to improvement in depressive symptoms.

neurofeedback protocols for depression introduce a new protocol, which aims to synthesize the best qualities of the currently available protocols; and present the results of a small clinical experiment with the new protocol. The most used protocols focus on Alpha inter-hemispheric asymmetry,

and Theta/Beta ratio within the left prefrontal cortex. A new protocol integrates both dimensions in a single circuit, adding to it a third programming line, which divides Beta frequencies and reinforces the decrease of Beta-3, in order to reduce anxiety. The favourable outcome of clinical experiment,

suggests that new research with this protocol is worthwhile. Challenges lie in the design of appropriate control conditions for rigorous clinical trials, and in the transfer of neurofeedback protocols from the laboratory to mobile devices to enhance the sustainability of any clinical benefits.

Integrating Neuroscience With Audio Visual Entrainment To Accelerate Behavioural Change - Treatment Of A 21 Year Old Woman Addicted To Gaming, Socially Isolated And Lacking Motivation.

Mr Joachim Lee is a Registered Clinical Counsellor and Supervisor with the Singapore Association for Counselling (SAC). He is also a Certified Clinical Hypnotherapist and Strategic Psychotherapist, with over 20 years of clinical experience. He is a certified neurofeedback therapist (SBCIA). He holds a Master in Counselling and is a qualified Solution-Oriented Therapist and Trainer.

He is the Director of MWS-Tampines Family Service Centre in Singapore for over 10 years.

In this conference, he is going to present a case which aims to describe a basic working and understanding of how the brain “pattern matches” (as described by Neuroscience) combined with the use of a basic Audio-Visual Entrainment (AVE) as an effective yet affordable treatment alternative to relying on a single treatment modality.

This approach involves applying the “APET” model, which is a practical application of knowledge derived from Neuroscience and collaboratively with the patient, co-create a personalised “empowering preferred future” to “pattern match” - a new response to override the old maladaptive behaviour and thinking, thus accelerating the process of change. The AVE is subsequently introduced as an adjunct to reinforce the positive changes and progress. This results in a self-directed motivation to act on the changes.

The outcome of this study suggests that while skilled delivery of the 'APET' model is necessary, the delivery of the AVE protocol requires minimal supervision and training, hence minimising the cost of treatment while delivering positive outcomes.

Neurotherapy For Clients Suffering From Anxiety, Obsessive-compulsive And Depressive Disorder

Tim is a psychologist and Certified Neurofeedback Practitioner. Tim is also certified at the Diplomate level by the International QEEG Certification Board. Tim has extensive therapeutic experience, working in private practice for more than 25 years. For the past 17 years this has included a special interest in neurotherapy.

In this presentation Tim will present case studies in which neurotherapy has been used for the treatment of anxiety and depressive disorders. In addition to changes in performance tests and rating scales there will be a focus on changes in cortical activation as measured by electroencephalography (EEG).

Neuroplasticity And Its Implications For The 21st Century Therapist

Dr. Subhasis is the inventor of the world's first fully wearable and connected brain plasticity training tool that trains Brain and Body as parts of ONE system. SynPhNe is the outcome of his PhD (Biomechatronics) study.

In this conference, he is going to talk about neuroplasticity and its implications for the 21st century therapist.

The past ten years of brain research has pointed increasingly towards the fact that physiotherapy, physical therapy, neurorehabilitation and occupational therapy are misrepresented as separate disciplines. The brain and body learn using simultaneous multi-modal inputs, often with substantial cross-modal overlaps. Brain training and muscle training are so deeply intertwined in each other that as we move into the domain of fine motor training and re-learning of motor skills after a stroke or injury, the boundary between where cognitive ability ends and physical ability begins gets increasingly blurred.

The need of the hour is for every therapist, no matter what their speciality, to think like “integrated care” therapists. This talk highlights the 5 key principles for the modern, 21st century therapist to consider in practice.

EEG Neurofeedback In Treating Epilepsy

Dr. K. Jayasankara Reddy is an associate professor at the Department of Psychology in Christ University in India.

In this conference, he is going to present on the use of neurofeedback for Epilepsy.

Epilepsy is a neurological disorder characterized by sudden recurrent episodes of sensory disturbance, loss of consciousness, or convulsions, associated with abnormal electrical activity in the brain. Nearly one third of patients with epilepsy do not benefit from medical treatment.

Last 20 to 30 years, researchers and clinicians have examined various behavioral and neurological approaches to the treatment of epilepsy. Electroencephalography neurofeedback therapy has been becoming recognized as one of the promising therapy for the epilepsy. Through this EEG neurofeedback, it is possible to train the brain to de-emphasize brain rhythms that lead to generation and propagation of seizure and emphasize rhythms that make seizures less likely to occur. One important concerns the effectiveness of neurofeedback, which entails the entrainment of specific electroencephalographic frequencies and amplitude for the purpose of decreasing seizure frequencies in patients with epilepsy. This presentation focusing based on his experience and the current literature on the efficacy of EEG neurofeedback in reducing seizure frequency and duration. While it is clear that EEG neurofeedback had a positive effect in most of the studies reviewed, these findings are limited due to multiple confounding factors in their techniques and protocols. The promising role of EEG neurofeedback as a treatment for epilepsy going discussed in detail in the presentation.

Assessment Of Psychosocial Stress Levels Using EEG

Ahmad Rauf Subhani received the BE degree in electrical engineering from Air University, Islamabad, Pakistan in 2009 and the MS degree from Universiti Teknologi PETRONAS (UTP), Malaysia in 2013. He is currently pursuing the PhD degree in electrical engineering from UTP.

From 2010 to 2011, he was a Research Assistant with the Center for Advanced Studies in Telecommunication (CAST). His research interests include biomedical signal processing, machine learning for rehabilitation.

In this conference, he is going to present on his and his team’s project on assessment of psychosocial stress levels using EEG.

Psychosocial stress is a mental state of too much expectation to perform under sheer pressure when a person can only marginally contend with the demands. These demands, either psychological or social, exist in daily life and result in poor quality of life by affecting people’s emotional behaviour, job performance, mental and physical health. Psychosocial stress is a leading cause of several psychophysiological disorders. For example, it increases the likelihood of depression, stroke, heart attack and cardiac arrest.

Conventional methods of the assessment of stress are subjective using questionnaires and interviews. This research is about the contemporary treatment of workplace stress based on objective indicators of stress using electroencephalography (EEG). They are interested in the identification of various levels of workplace stress. They have recruited the subjects in five levels of stress due their job. In the recruitment process they had the assistance of expert psychiatrist who interviewed the subjects to verify that their job routines were causing stress. The study hypothesizes that various levels of stress cause changes in the brain that can be reflected the basic building blocks of EEG. They are also interested to see stress-related changes reflected in cortisol – a hormone that is active during stress.

Meanwhile, they worked on existing EEG database of stress that was collected while inducing mental stress in non-stress subjects. They proposed a machine learning-based framework for the objective identification of levels of stress based on EEG signals from normal subjects under mental stress. In this framework, a novel methodology to detect mental stress levels by exploring quantitative differences between stress and control conditions as well as four levels of stress conditions was presented. They explored various features such absolute power, relative power, coherence, amplitude asymmetry and phase lag. Furthermore, tested different combinations of feature selection and classification techniques. This framework achieved an accuracy of 94.58% in classifying stress vs. control and 83.4% accuracy in classifying four levels of stress.

The results support the notion that psychosocial stress alters the brain dynamics and that EEG can be used to gauge such changes in the brain. Moreover, EEG based system are suitable for clinical treatment of stress due to their mobility and compact size.

Electroencephalography (EEG)-based Diagnosis And Treatment Selection For Major Depressive Disorder (MDD)

Dr. Wajid Mumtaz is a PhD candidate attached with center for intelligent signal and imaging (CISIR), university Teknology PETRONAS, Malaysia. His research interest include neuri-signal processing, machine learning for diagnosis.

Major Depressive Disorder (MDD), a leading cause of functional disability worldwide, is a mental illness and commonly known as unipolar depression. The clinical management of MDD patients has been challenging that includes an early diagnosis and antidepressant’s treatment selection. The electroencephalography (EEG)-based studies for diagnosis and treatment selection have shown less clear clinical utilities and warrant further investigations. This research advocates the use of EEG as a biomarker for early diagnosis and antidepressant’s treatment selection for unipolar MDD patients. More specifically, the study has presented an improved feature selection and classification system involving pre-treatment EEG data termed as Intelligent Treatment Management System (ITMS) for unipolar depression. The ITMS involved an integration of the most significant EEG features as input data.

The study hypothesized that the MDD patients and healthy controls could be discriminated based on integrating the EEG alpha asymmetry and synchronization likelihood (the EEG measure to quantify the brain functional connectivity). The method helped during diagnosis of MDD patients and was termed as ITMS for diagnosis (ITMS-diagnosis).

In addition, the study hypothesized that the integration of the time and frequency information involving wavelet transform (WT) analysis and EEG signal complexity measures (composite permutation entropy index, sample entropy, and fractal dimension) could discriminate the antidepressants treatment response and non-response. The method helped during antidepressant’s treatment selection such as classifying MDD patients as either respondents or non-respondents to treatment with selective serotonin re-uptake inhibitors (SSRIs): Escitalopram (E), Fluvoxamine (F), Sertraline (S), Fluoxetine (Fl), and was termed as the ITMS for treatment selection (ITMS-treatment selection).

The proposed ITMS for depression includes a general machine learning (ML) framework for EEG feature extraction, the selection of most noteworthy features that could give high-performance classification models such as the logistic regression (LR), support vector machine (SVM) and naïve bayesian (NB) classifiers. Moreover, the proposed methods have been validated with EEG data involving 34 MDD patients (medication-free) with a confirmed diagnosis of depression and a group of 30 age-matched healthy controls. In addition, the proposed method was validated with 10-fold cross validation (10-CV).

Consequently, the EEG features for diagnosis such as the power of alpha band, alpha interhemispheric asymmetry, synchronization likelihood and EEG features for treatment selection such as wavelet-based signal energy, wavelet-based sample entropy, wavelet-based fractal dimension, and wavelet-based composite permutation entropy index were extracted from the frontal and temporal regions. The features were found significant for both the MDD diagnosis and the antidepressant’s treatment selection. Furthermore, the proposed SVM method exhibited diagnosis accuracy = 98.8%, sensitivity=98.6%, specificity=99.4%, and F1-score=0.98. In addition, the proposed SVM method predicted the treatment outcome with an accuracy = 89.1%, sensitivity=91%, specificity=88.7%, and F1-score=0.90. Moreover, the brain regions such as the frontal and temporal have shown relevance with the depression.

In conclusion, the classification results have proven the proposed EEG-based ITMS to be used for diagnosis and treatment selection for the MDD patients. In addition, it is clinically suitable for the diagnosis and treatment selection at week 0 (pretreatment).

Brain Effective Connectivity Using EEG Signals For Early Prediction Of Epileptic Seizure

Danish is currently a Research Scholar at Center for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS, Malaysia pursuing his PhD on prediction of epileptic seizure through effective connectivity using EEG. He is also a faculty member in Department of Electronic and Telecommunications Engineering at NED University of Engineering and Technology, Pakistan since March 2010. He received his Bachelor and Master in engineering degrees in Telecommunications from NED University of Engineering and Technology, Pakistan, in 2009 and 2012 respectively. His research interests include signal processing, EEG, seizure prediction and image processing.

Millions of lives in this world are affected by epilepsy, a common and complex neurological disorder in which aberrant behavior of brain that includes abnormal excessive or synchronous activity of neurons causes epileptic seizure. This not only impacts the quality of people’s lives but also increases death risks in patients. Globally, an estimated 2.4 million people are diagnosed with epilepsy each year. Approximately 50 million people currently live with epilepsy worldwide. While it is manageable in certain patients using prescribed drugs, studies have shown that there are about 20-30% of people whose epileptic seizures are likely to relapse after initial remission. Besides, there are some who develop drug resistant epilepsy.

Unfortunately, there is still a gap in understanding of the neurophysiological mechanisms that lead to seizures. Data interpretation of electroencephalogram (EEG) gives useful information regarding neural activity of the brain with high temporal resolution (milliseconds). Thus, allowing it to be a promising modality to predict seizure at the earliest for proper intervention necessary to prepare patient for upcoming seizure or even to stop it. Over the last 35 years, multitude of algorithms have been developed in numerous centers all over the world for epileptic seizure prediction. However, no class 1 evidence of clinical usefulness (prospective, blinded, and randomized) exists.

Billions of neurons in brain form structural and functional connectivity based on the task in hand. Along with functional connectivity which measures temporal correlation between remote neurophysiological events, effective connectivity that gives the influence one neural system exerts over another, provides a remarkable insight to the brain and provides information about interconnected components during different tasks.

In this study, it is hypothesized that effective connectivity of different brain areas varies before actual occurrence of seizure and there is an underlying pattern in the chaotic nature of brain before and during the occurrence of epileptic seizure. Thus, this study aims to investigate chaotic nature of brain and find effective connectivity before and during the seizure to develop an algorithm which will help in prediction of epileptic seizure at the earliest with minimum false positive rate.

The proposed prediction algorithm will be based on stored EEG data set from Campus Technologies Freiburg Gmbh, Germany. Effective connectivity using Partial Directed Coherence (PDC), frequency domain variant of granger causality method, will be calculated using sliding window with a size duration of 4 sec and statistical parameters will be observed during normal, pre-ictal and ictal phases. The changes in these statistical parameters will be analyzed to find out precursor(s) for the prediction of epileptic seizure. This will allow calculation of probability of upcoming seizure based on which the alarm for impending seizure may be set.

Treating Inattention And Impulsivity In Children With Autism Using Neurofeedback

Jerry is a certified neurofeedback therapist (SBCIA) and has worked as a Neurofeedback therapist since 2009 at an autistic centre, the Lions REACh, Penang, Malaysia.

Neurofeedback therapy is gaining recognition as an alternative and innovative treatment for children with special needs such as attention deficit hyperactivity disorder, learning disability and autism. Common issues among children with autism are inattention, poor communication and understanding of language and impulsivity. In this presentation, three cases studies were selected to examine the effectiveness of neurofeedback therapy. Feedback from parents and teachers stated in agreement that the children showed improvement in attention, awareness of surrounding;

information processing, speech and language, and reduce of impulsivity. In conclusion, neurofeedback therapy increases the attention and awareness of surrounding for children with autism, which in turn improved their learning at other areas.

QEEG Analysis Of Heart Rate Variability Biofeedback: An Implication For The Optimal Learning State

Eleanor is a social science graduate from the National University of Singapore. She is a certified neurofeedback therapist (SBCIA) and is currently a consultant at Spectrum Learning Pte Ltd, Singapore. She is a conversational hypnotist and also trains people in Heart Rate Variability (HRV) Biofeedback.

She is interested to find out what happens to the EEG of a person when they are doing HRV biofeedback and what are the implications of the findings to neurofeedback. She went on to explore this with 4 subjects including herself and have very interesting findings and insights beyond what she set out to explore.

She is very excited to share these findings and insights from various perspectives, as a neurofeedback therapist, hypnotist and HRV biofeedback trainer.