Investigating ECoG Signal Acquisition And Transmission in Brain Computer Interface Applications
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Investigating ECoG Signal Acquisition And Transmission in Brain Computer Interface Applications

  • Author(s): Ajrawi, Shams Al
  • Advisor(s): Sarkar, Mahasweta;
  • Rao, Ramesh
  • et al.
Abstract

Brain-Computer Interface (BCI) can permit individuals to use their thoughts as the sole means to control objects such as smart homes and robots. Although, the main goal of BCI system is to bring back mobility to severely paralyzed people and give them another way to communicate, a way that does not depend on muscle control.While BCI is a promising interdisciplinary tool, researchers are confronting signal acquisition and signal transmission as an obstacle to further development. For signal acquisition, we proposed two approaches to extract and classify brain activities for BCI competition III data-set I, they are Hierarchical recursive feature elimination (HRFE) and Flexible Analytic Wavelet Transformation (FAWT), we evaluated them in terms of accuracy and classification time. This process requires efficient transmission of Electro-cartographical (ECoG) signal from implanting electrodes inside the brain to an external receiver located outside of the scalp. In particular, the generated artifacts due to in-phase/quadrature (I/Q) imbalance of utilizing down converter, and time interleaved analog-to-digital converters (ADCs) may lead to significant interference to desired signal which affects the detection performance. In this thesis, an efficient, low, complexity, balance technique is proposed for BCI communications to mitigate the interference using the adaptive least mean square (LMS) algorithm. The performance results of the conducted experiments on a phantom human brain model are shown to validate the designed scheme compared with the existing BCI approach. For Signal transmission, investigated the feasibility of passive Ultra High Frequency Radio Frequency Identification (UHF-RFID) for wireless communication between multiple transmitters inside the brain that collect vital data continuously and transmit them to an external controller using Back-scatter technique and we proposed a novel Medium Access Control (MAC) protocols that can be utilized for dependable transmission in BCI applications by altering their control parameters. However, modifying these parameters is another source of concern due to the scarcity in knowledge about the effect of modifications. In this thesis, a hybrid MAC protocol is proposed. Enhancing data gathering in medical records has been considered by proposing SC (Scanner Controller) device which consist of mini-reader, GPS and timer integrated together for every patient.

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