Ashley, Adrian (2021) Improving classification of error related potentials using novel feature extraction and classification algorithms for an assistive robotic device. MPhil thesis, University of Sheffield.
Abstract
We evaluated the proposed feature extraction algorithm and the classifier, and we showed that the performance surpassed the state of the art algorithms in error detection. Advances in technology are required to improve the quality of life of a person with a severe disability who has lost their independence of movement in their daily life. Brain-computer interface (BCI) is a possible technology to re-establish a sense of independence for the person with a severe disability through direct communication between the brain and an electronic device. To enhance the symbiotic interface between the person and BCI its accuracy and robustness should be improved across all age groups. This thesis aims to address the above-mentioned issue by developing a novel feature extraction algorithm and a novel classification algorithm for the detection of erroneous actions made by either human or BCI. The research approach evaluated the state of the art error detection classifier using data from two different age groups, young and elderly. The performance showed a statistical difference between the aforementioned age groups; therefore, there needs to be an improvement in error detection and classification. The results showed that my proposed relative peak feature (RPF) and adaptive decision surface (ADS) classifier outperformed the state of the art algorithms in detecting errors using EEG for both elderly and young groups. In addition, the novel classification algorithm has been applied to motor imagery to improve the detection of when a person imagines moving a limb. Finally, this thesis takes a brief look at object recognition for a shared control task of identifying utensils in cooperation with a prosthetic robotic hand.
Metadata
Supervisors: | ARVANEH, Mahnaz and MIHAYLOVA, Lyudmila |
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Related URLs: | |
Keywords: | BCI, Brain Computer Interface, ErrP, ERN |
Awarding institution: | University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Mr Adrian Ashley |
Date Deposited: | 18 Feb 2021 23:21 |
Last Modified: | 18 Feb 2021 23:21 |
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