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The Brain-Computer Interface (BCI) is the technology that enables direct communication between the human brain and the external devices. Electroencephalography (EEG) proves to be the most studied measure of recording brain activity in BCI design. The paper is intended to analyze and extract the features of EEG signal and to classify the signal so that human emotions can be discriminated and serve as the control signal for BCI. The proposed method involves EEG data acquisition and processing which is done by feature extraction and classification of features at different frequency levels for Beta, Alpha, Theta and Delta waves. The Principal Component Analysis(PCA ),and the Wavelet Transform(WT) can be used for dimensionality reduction and feature extraction . The Artificial Neural Network (ANN) which is a computationally powerful model, is used as the classifier. The paper presents the comparison between the two approaches PCA and WT applied on the ANN Classifier.

Use of scalp EEG for the diagnosis of various cerebral disorders is progressively increasing. Though the advanced neuroimaging techniques such as MRI and CT-SCAN still stay as principal confirmative methods for detecting and localizing brain tumors, the development of automated systems for the detection of brain tumors using the scalp EEG has started attracting the researchers all over the world notably since 2000. This is because of two important facts: (i) cheapness and easiness of methods of recording and analyzing the scalp EEG and (ii) lower risk and possible early detection. This paper presents a method of detecting the brain tumor using the first, second and third order statistics of the scalp EEG with a Modified Wavelet-Independent Component Analysis (MwICA) technique and a multi-layer feed-forward neural network.

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Human speech becomes impaired i.e., unintelligible due to a variety of reasons that can be either neurological or anatomical. The objective of the research was to improve the intelligibility and audibility of the impaired speech that resulted from a disabled human speech mechanism with impairment in the acoustic system-the supra-laryngeal vocal tract. For this purpose three methods are presented in this paper. Method 1 was to develop an inverse model of the speech degradation using the Cepstral technique. Method 2 was to replace the degraded vocal tract response by a normal vocal tract response using the Cepstral technique. Method 3 was to replace the degraded vocal tract response by a normal vocal tract response using the Linear Prediction technique