EEG based controls are extensively used in applications such as autonomous navigation of remote vehicles and wheelchairs, as prosthetic control for limb movements in health care, in robotics and in gaming. The work aimed at implementing and classifying the intended controls for autonomous navigation, by analyzing the recorded EEG signals. Here, eye closures extracted from the EEG signals were pulse coded to generate the control signals for navigation. The EEG data was acquired using wireless Emotive Epoc EEG headset, with 14 electrodes, from ten healthy subjects. Preprocessing techniques were applied to enhance the signal, by removing noise and baseline variations. The features from the blinks considered were height of the ocular pulses and their respective widths, from four channels. K-Nearest Neighbor Classifier and Artificial Neural Network Classifier were applied to classify the number of blinks. The results of the study showed that, for the data set under consideration, ANN Classifier gave 98.58% accuracy and 94% sensitivity, compared to KNN Classifier, which gave 96.06 % accuracy and 87.42% sensitivity, to classify the blinks for the control application.

Speech based language identification system has a wide range of applications in the field of telephone services, multilingual translation services, government intelligence and monitoring etc. Identifying the exact speech feature for classification is an important problem in the language identification research area. In this work, we are comparing the performance measures of a language identification system using two different supervised learning algorithms. Mel frequency cepstral coefficients and formant feature vectors are extracted for classification purpose. The system which is developed using the database of seven different Indian languages is capable of identifying languages with LDA giving a maximum classification accuracy of 93.88% when compared to SVM with a classification accuracy of 84%.