Improved time-frequency features and electrode placement for EEG-based biometric person recognition

This work introduces a novel feature extraction method for biometric recognition using EEG data and provides an analysis of the impact of electrode placements on performance. The feature extraction method is based on the wavelet transform of the raw EEG signal. The logarithms of wavelet coefficients are further processed using the discrete cosine transform (DCT). The DCT coefficients from each wavelet band are used to form the feature vectors for classification. As an application in the biometrics scenario, the effectiveness of the electrode locations on person recognition is also investigated, and suggestions are made for electrode positioning to improve performance. The effectiveness of the proposed feature was investigated in both identification and verification scenarios. Identification results of 98.24% and 93.28% were obtained using the EEG Motor Movement/Imagery Dataset (MM/I) and the UCI EEG Database Dataset respectively, which compares favorably with other published reports while using a significantly smaller number of electrodes. The performance of the proposed system also showed substantial improvements in the verification scenario when compared with some similar systems from the published literature. A multi-session analysis is simulated using with eyes open and eyes closed recordings from the MM/I database. It is found that the proposed feature is less influenced by time separation between training and testing compared with a conventional feature based on power spectral analysis

Radar sensing for ambient assisted living application with artificial intelligence

In a time characterized by rapid technological advancements and a noticeable trend towards an older average population, the need for automated systems to monitor movements and actions has become increasingly important. This thesis delves into the application of radar, specifically Frequency Modulated Continuous Wave (FMCW) radar, as an emerging and effective sensor in the field of "Activity Recognition." This area involves capturing motion data through sensors and integrating it with machine learning algorithms to autonomously classify human activities. Radar is distinguished by its ability to accurately track complex bodily movements while ensuring privacy compliance. The research provides an in-depth examination of FMCW radar, detailing its operational principles and exploring radar information domains such as range-time and micro-Doppler signatures. Following this, the thesis presents a state-of-the-art review in activity recognition, discussing key papers and significant works that have shaped the field. The thesis then focuses on research topics where contributions were made. The first topic is human activity recognition (HAR) with different physiology, presenting a comprehensive experimental setup with radar sensors to capture various human activities. The analysis of classification results reveals the effectiveness of different radar representations. Advancing into the domain of resource-constrained system platforms. It introduces adaptive thresholding for efficient data processing and discusses the optimization of these methods using artificial intelligence, particularly focusing on the evolution algorithm such as Self-Adaptive Differential Evolution Algorithm (SADEA). The final chapter discusses the use of Long Short-Term Memory (LSTM) networks for short-range personnel recognition using radar signals. It details the training and testing methodologies and provides an analysis of LSTM networks performance in temporal classification tasks. Overall, this thesis demonstrates the effectiveness of merging radar technology with machine learning in HAR, particularly in assisted living. It contributes to the field by introducing methods optimized for resource-limited settings and innovative approaches in temporal classification using LSTM networks