C. W. N. F. Che Wan Fadzal

An analysis of EEG signal generated from grasping and writing

Electroencephalogram consists of hand movement information that can be extracted using suitable digital signal processing techniques. In this study, the EEG signals generated from hand grasping and writing were recorded from 4 channels; C3, C4, P3 and P4 and filtered using band pass filter with frequency range of 8 Hz to 30 Hz. The signal was then analysed using Fast Fourier Transform. Analysis of EEG signals showed that both hand grasping and writing produced signals with beta frequency.

Review of brain computer interface application in diagnosing dyslexia

Dyslexia is one of brain disorders which needs to be detected at an early stage to allow the children to master the basic and to avoid damage to self esteem and self-confidence. In this paper, treatment of dyslexia and researches on dyslexia diagnosis are discussed. Neuro-feedback has high potential to diagnose dyslexia. It has been proven that neuro-feedback is able to improve spelling disorder. Therefore, investigation on the performance of neuro-feedback in diagnosing and treating reading disorder should be carried out.

Analysis of EEG signal from right and left hand writing movements

This paper studies on the characteristics of electroencephalogram (EEG) which generated from writing using right and left hand. The EEG signals were recorded from 4 channels, C3, C4, P3 and P4 and processed using band pass filter (8-30Hz). Two method of analysis were performed; Fast Fourier transform and power spectral density. The results showed that Power Spectral Density can be used to distinguish right and left hand writing movements from EEG signals.

Spectral analysis of EEG signals generated from imagined writing

Movement imagination is one of the ways that can produce electroencephalogram (EEG). Imagined writing may help to cure writing disability in children if the EEG signal obtained from this activity is used in the therapy system. This paper describes the spectral analysis of EEG signals obtained during actual writing and imagined writing. It also reveals the difference in frequency of EEG signal obtained from relax condition, actual writing and imagined writing. The spectral analysis results showed that the EEG signals from imagined writing has the same frequency range as that generated from the actual writing.

Short-time Fourier Transform analysis of EEG signal from writing

Short-time Fourier Transform (STFT) provides an advantage of revealing the frequency contents of the signal at each time point in the signal. This information can be used to provide control and perform several tasks in Brain Computer Interface system. This paper describes the STFT analysis of EEG signals obtained during relaxing and writing, The results of the STFT analysis showed that there are significant differences in the frequency components and patterns between the EEG signals obtained from relax and writing.

Comparison between characteristics of EEG signal generated from dyslexic and normal children

Electroencephalogram (EEG) is one of the methods to detect dyslexia in children. Dyslexia has to be detected at an early stage to help the children to excel in their study and later be successful in life. In this study, the EEG signals generated from dyslexic and normal children during relax and writing words were processed, analysed and compared. Four electrodes; C3, C4, P3 and P4 were used in the recording of the EEG signals. The recorded EEG signals were filtered using a band pass filter with frequency range of 8 - 30 Hz. The signal was then analyzed using Fast Fourier Transform. Analysis of EEG signals showed that the range of frequency of EEG signals during writing for dyslexic was greater than that of normal children for each electrode placement at beta sub band frequency. The range of frequency of EEG signals for dyslexics is 22-28 Hz whereas for normal children is 14-22 Hz.

Frequency analysis of EEG signal generated from dyslexic children

Dyslexia is a neurological disorder which needs to be detected at an early stage to know their specific needs and to help them cope with the problem. One of the ways to detect dyslexia is by using Electroencephalogram (EEG). In this study, the EEG signals recorded from dyslexics children while performing writing activities were analyzed. The EEG signals were recorded from 4 channels; C3, C4, P3 and P4 and filtered using band pass filter with frequency range 8 Hz to 30 Hz. The signal was analyzed using Fast Fourier Transform. Analysis of EEG signals showed that the range of frequency for dyslexic children during writing is 22-28 Hz which is considered high and indicates that they are trying hard to write a correct word.

EEG-based time and spatial interpretation of activation areas for relaxation and words writing between poor and capable dyslexic children

Symptoms of dyslexia such as difficulties with accurate and/or fluent word recognition, and/or poor spelling as well as decoding abilities, are easily misinterpreted as laziness and defiance amongst school children. Indeed, 37.9% of 699 school dropouts and failures are diagnosed as dyslexic. Currently, Screening for dyslexia relies heavily on therapists, whom are few and subjective, yet objective methods are still unavailable. EEG has long been a popular method to study the cognitive processes in human such as language processing and motor activity. However, its interpretation is limited to time and frequency domain, without visual information, which is still useful. Here, our research intends to illustrate an EEG-based time and spatial interpretation of activated brain areas for the poor and capable dyslexic during the state of relaxation and words writing, being the first attempt ever reported. From the 2D distribution of EEG spectral at the activation areas and its progress with time, it is observed that capable dyslexics are able to relax compared to poor dyslexics. During the state of words writing, neural activities are found higher on the right hemisphere than the left hemisphere of the capable dyslexics, which suggests a neurobiological compensation pathway in the right hemisphere, during reading and writing, which is not observed in the poor dyslexics.

Development of computer-based assessment for brain electrophysiology technique of dyslexic children

This paper present the design and delivery procedure for a computer based assessment for brain electrophysiology technique of dyslexic children. Electrical activities of the brain are recorded using electroencephalograph where analysis can then be made and results can lead towards an objective diagnosis assessment of dyslexia. The benchmark of the assessment design is based on methods employed by the Dyslexia Association of Malaysia. There are six tasks in the overall assessment that looks into the subject visual and phonics processing, along with their reading, writing and spelling proficiencies. Eight electrode positions were applied on the scalp based on the brain learning pathway to look into areas of activation, C3, C4, CZ, P3, P4, F8, F7 and T7. The computer based assessment provide the basis of activities to be presented to a dyslexic subject with an age range of 7 to 12 that evaluates their learning capabilities and present the marker that can lead towards the diagnosis of dyslexia. Coupled with EEG recording, it provides a view on the working of the brain giving information for practitioner in the design of pedagogical approach or even neurofeedback protocol.

Classification of EEG Signals from Imagined Writing for Treating Writing Disorder

The occurrence of imagined writing event can be recognised from Electroencephalogram (EEG) when the signal is passed through several processes including feature extraction and classification. The imagined writing process may be useful for treating writing disorder if the activity is repeated several times. A technique called Autoregressive (AR) is able to model the EEG signal with imagined writing activity and produce coefficients that can be used as input feature for Multi Layer Perceptron (MLP). This study investigates the optimum AR order combined with MLP in classifying EEG signals from imagined writing letters that dyslexic children often get confused. Result shows that there is distinct pattern in EEG dataset during imagined writing which can be discriminated between different tasks. The best AR order for classifying EEG signals from imagined writing activity is between 16 to 20 orders.