Analysis of Potential Types of Brain-Computer Interface Technology for a Severely Locked-in Patient

Author: Fatemeh Khazab

Khazab, Fatemeh, 2016 Analysis of Potential Types of Brain-Computer Interface Technology for a Severely Locked-in Patient, Flinders University, School of Computer Science, Engineering and Mathematics

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This study will assess a patient suffering from brain damage over decades to fit with a Brain Computer Interfacing (BCI) technology. These techniques will replace conventional muscle controls with direct brain control of outside assistive devices such as wheelchair. This would help people with severe impairments to live happier and more productive lives. As part of this study, patient’s clinical background was investigated. Considering patient’s abilities and strengths, a protocol was designed. The protocol included all practised BCI techniques in literature to find feasibility of implementing a fitting the patient with BCI technology. This research studied auditory, visual and tactile steady state responses, auditory and visual evoked potentials and visual, audio and cross modal (audio-visual) oddball responses. Results obtained suggested lesions in the brain interrupted the operation of some sensory systems. Techniques used to access BCI feasibility for this patient suggested presence of visual steady state response and possibly very weak respond to cross modal oddball tasks. None of the results in the other modalities of the steady state test response and oddball test indicated presence of response. The two sections with possibly positive results may be further investigated in order to find if the results found can be focus related and reliably reproduced.

Keywords: Brain Injury ,paralysed ,locked-in, consciousness ,awareness ,abilities ,vision ,hearing ,sensory , current brain assessments for consciousness ,BCI for diagnose and treatment , communication, mobility ,control ,assistive technologies ,BCI as assistive technology
Subject: Engineering thesis, Computer Science thesis

Thesis type: Masters
Completed: 2016
School: School of Computer Science, Engineering and Mathematics
Supervisor: Prof David Powers