In recent years, EEG-based navigation and communication systems for differentially enabled communities have been progressively receiving more attention. To provide a navigation system with a communication aid, a customized protocol using thought evoked sensor potentials has been proposed in this research work to aid the differentially enabled communities. This study presents the higher order robotic sensor spectra-based features to categorize seven basic tasks that include Forward, Left, Right, Yes, NO, Help and Relax; that can be used for navigating a robot chair and also for communications using an oddball paradigm. The proposed system records the eight-channel wireless electroencephalography signal from ten subjects while the subject was perceiving seven different tasks. The recorded brain wave signals are pre-processed to remove the interference waveforms and segmented into six frequency band signals, i.e. Delta, Theta, Alpha, Beta, Gamma1-1 and Gamma-2. The frequency band signals are segmented into frame samples of equal length and are used to extract the features using bispectrum estimation. Further, statistical features such as the mean of bispectral magnitude and entropy using the bispectrum region are extracted and formed as a feature set. The extracted feature sets are tenfold cross validated using multilayer neural network classifier. From the results, it is observed that the entropy of bispectral magnitude feature based classifier model has the maximum classification accuracy of 84.71% and the mean of the bispectral magnitude feature based classifier model has the minimum classification accuracy of 68.52 %.

The English language as spoken by Malaysians varies from place to place and differs from one ethnic community and its sub-group to another. Hence, it is necessary to develop an exclusive speech to text translation system for understanding the English pronunciation as spoken by Malaysians. Speech translation is a process of both speech recognition and equivalent phonemic to word translation. Speech recognition is a process of identifying phonemes from the speech segment. In this paper, the initial step for speech recognition by identifying the phoneme features is proposed. In order to classify the phoneme features, Mel-frequency cepstral coefficients (MFCC) are computed in this paper. A simple feed forward neural network (FFNN) trained by back propagation procedure is proposed for identifying the phonemes features. The extracted MFCC coefficients are used as input to a neural network classifier for associating it to one of the 11 classes.

Accent recognition is one of the most important topics in automatic speaker and speaker-independent speech recognition (SI-ASR) systems in recent years. The growth of voice-controlled technologies has becoming part of our daily life, nevertheless variability in speech makes these spoken language technologies relatively difficult. One of the profound variability is accent. By classifying accent types, different models could be developed to handle SI-ASR. In this paper, we classified three accents in English language recorded from three main ethnicities in Malaysia namely Malay, Chinese and Indian using artificial neural network model. All experiments were performed in speaker-independent and three most accent-sensitive words-independent modes. Mel-bands spectral energy was extracted from eighteen bands taking the statistical values of each speech sample i.e. mean, standard deviation, kurtosis and the ratio of standard deviation to kurtosis to characterize the spectral energy distribution. The system was evaluated using independent test dataset, partial-independent test dataset and training dataset. The best three-class accuracy rate of 99.01% with independent test dataset was obtained. The overall accuracy rate for several trials was averaged to 96.79% with the average learning time at 49 epochs.

In this research work, a Thought Controlled Intelligent robot chair with communication aid has been developed. A simple brain wave data acquisition protocol has been developed with seven basic tasks that can be used for both robot chair control and communication system. The proposed system records the 19-channel electroencephalography signal while mentally executing the tasks. The recorded brain wave signals are pre-processed to remove the interference waveforms and segmented into four frequency bands. The frequency band signals are used to extract the features using Band Power features to optimize the electrode channels using a combination without repetition analysis. Hence, the raw brain wave signals of the selected electrode channels are pre-processed and used to extract the cross-correlation coefficients between any two frequency bands. Similarly, six permutation sets of four frequency bands for each electrode position are framed and the statistical features such as Minimum, Maximum, Mean and Standard Deviation are computed to form the customized feature set. The extracted feature sets are classified using Multilayer Neural Network. Further, the classification models are compared and from the results it is observed that the maximum feature set hits the highest classification accuracy of 88.49%.