Lee Yoot Khuan

A review of signal processing in brain computer interface system

Brain computer interface system incorporating signal processing techniques augments human capabilities by providing a new interaction link with the outside world and is particularly relevant as an aid for paralyzed humans, and even for able-bodied people. This paper describes the essential components of BCI system, types and signal processing techniques used in the system. Several methods of electrode placement, filtering, feature extraction and EEG signal classifications are also discussed. Future improvements on BCI system are proposed based on the limitations that have been highlighted.

Classification of Infant Cries with Asphyxia Using Multilayer Perceptron Neural Network

Asphyxia occurs in infants with neurological level disturbance, which is found to affect sound of cry produced by infants. The infant cry signals with asphyxia have distinct patterns which can be recognized with pattern classifiers such as Artificial Neural Network (ANN). This study investigates the performance of the Multilayer Perceptron (MLP) classifier in discriminating between healthy and infants with asphyxia from their cries, of ages from zero to seven months old, with an input feature reduction algorithm, Orthogonal Lest Square (OLS) analysis, in contrast to direct selection. The infant cry waveform served as input to Mel Frequency Cepstrum (MFC) analysis for feature extraction. The MLP classifier performance was examined with different combination in number of coefficients, filter bank and hidden nodes. It is found that the OLS algorithm is effective in enhancing the accuracy of MLP classifier while reducing the computation load. Both the average and highest MLP classification accuracies with coefficients being ranked by OLS algorithm have consistently displayed better score than those by direct selection. The highest MLP classification accuracy of 94% is obtained with 40 filter banks, 12 highly ranked MFC coefficients and 15 hidden nodes.

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.

Design and Fabrication of Polysilicon‐based Piezoresistive Microcantilever For Biological Sensing

This paper describes the fabrication process of a piezoresistive microcantilever sensor that is used as a platform for biological sensing such as salivary amylase‐activity. The 0.5 μm‐thick piezoresistive sensors are made on polysilicon‐based cantilever beam. This surface micromachined microcantilever is based on silicon wafers and fabricated using 0.5 μm CMOS process technology . The range of microcantilevers is 40–140 μm long, 0.5–1 μm thick, and 40 μm wide. The force sensitivity of implemented sensors ranges from 2–10 Pa is corresponding to salivary amylase‐activity adsorbed on the piezoresistive microcantilever.

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.

Classification of infant cries with hypothyroidism using Multilayer Perceptron neural network

Hypothyroidism occurs in infants with insufficient production of hormones by the thyroid gland. The cry signals of babies with hypothyroidism have distinct patterns which can be recognized with pattern classifiers such as Multilayer Perceptron (MLP) artificial neural network. This study investigates the performance of the MLP in discriminating between healthy infants and infants suffering from hypothyroidism based on their cries. The infant cries were first divided into one second segments, and important features were extracted using Mel Frequency Cepstrum Coefficient (MFCC) analysis. Two methods were then used to select which MFCC coefficients to be used as features for the MLP: direct selection or Fishers Ratio analysis (F-ratio analysis). Their performances were compared with experimental results showing that MLP was able to accurately distinguish between the two cases. The classification performance of MLP trained with F-Ratio analysis is found to be better compared to direct selection method.

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.

Investigation of Mel Frequency Cepstrum Coefficients parameters for classification of infant cries with hypothyroidism using MLP classifier

Hypothyroidism in infants is caused by insufficient production of hormones by the thyroid gland. Due to stress in the chest cavity due to the enlarged liver, the cry signals are unique and can be distinguished from healthy infant cries. We investigate the usage of the Multilayer Perceptron (MLP) classifier to diagnose infant hypothyroidism. The Mel Frequency Cepstrum Coefficients (MFCC) feature extraction method was used to extract important information from the cry signal itself. This study investigates the number of filter banks and coefficients in MFCC to extract optimal information from infant cry signals, to be classified using MLP. The cry signals were first divided into equal-length segments, and MFCC was used to extract features from them. Tests on the combined University of Milano-Bicocca and Instituto Nacional de Astrofisica datasets yielded MLP classification accuracy of 89.18%, suggesting that the optimal MFCC resolution was obtained using 36 filter banks, and 19 coefficients.

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.

Development of a low cost self-sensing Piezoresistive Microcantilever biosensor and read-out circuitry for measuring salivary-amylase activity

This paper deals with the development of a low cost self-sensing Piezeoresistive Microcantilever Biosensor to detect human stress using salivary alpha amylase activity. The salivary alpha amylase enzyme will immobilize on to the Piezeoresistive Microcantilever biosensor to detect activity during the enzyme responses. When the Microcantilever beam deflects it caused the stress change within the microcantilever beam and applied strain to the piezoresistor material thereby causing the resistance change and the Piezoresistive Microcantilever biosensor integrated with transducer components coverts the biochemical signal into measurable signal. The enzyme concentration signal is converted to a voltage by the transducer. A novel Piezeoresistive Microcantilever biosensing method and its application is described. The device was designed specifically that it enables the small resistivity change due to the enzymatic reaction to be measured.