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Brain-computer interface (BCI) technology provides a means of communication that allows individuals with severely impaired movement to communicate with assistive devices using the electroencephalogram (EEG) or other brain signals. Dealing with the unknown embedded noise within the raw EEG and the inherent lower bandwidth of BCI are still two of the major challenges in making BC! practical for day-to-day use. The raw EEG signal recorded non-invasively during motor i ~~~y (MI) is intrinsically " ." embedded with non-Gaussian noise while the actual noise-free EEG has so far not been attained. Therefore, a filtering approach is needed to remove noise. A novel quantum mechanics motivated alternative neural information processing architecture using the Schrodinger wave equation (SWE) is proposed to filter and thereby enhance the information from the otherwise noisy EEG signals. This novel filtering approach is constructed using a layer of neurons within the neural network framework, referred to as the Recurrent Quantum Neural Network (RQNN) that recurrently computes a time-varying probability density function (pdf) for the measurement of the observed signal. The raw EEG sample is encoded in terms of a particle-like wave packet that can be used to accurately filter noise from the EEG using an unsupervised learning scheme without making any assumption about the underlying distribution. The RQNN enhanced EEG signal is more easily classified than the raw signal. Another major challenge in two-class BC! systems is also addressed in this thesis, namely the inherent lower bandwidth of the communication channel that may lead to a sluggish response in suitably controlling a mobile robotic device. An intelligent and adaptive user interface, which plays a very important role as a front-end display for the BCI user is proposed. The framework of the proposed intelligent Adaptive User Interface (iAUI) i.e., the brain-robot interface is consistent for a range of applications e.g., for controlling either a mobile robot or a robotic arm. The iAUI for mobile robot offers a real-time prioritized list of all the options for selection by the user. Prioritized update ofthe iAUI is possible by utilizing information obtained from the sonar sensors mounted on the mobile robot. Through iAUI the user is always offered the most likely choice, thereby improving the information transfer rate. Similarly, the interface for controlling the robot arm displays the list of available objects for user selection depending on the real-time information from the camera view of the robot arm. Results on multiple participants show that both the main contributions, the RQNN filtering and the iAUI address to a large extent the issues of dealing with unknown embedded noise within the raw EEG and the inherent lower bandwidth of BCI. xv Abstract

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