Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/519603
Title: | Brain computer interface eeg signal processing with new approaches of feature extraction and classification |
Researcher: | Mary Judith, A |
Guide(s): | Baghavathi priya, S |
Keywords: | Brain computer interface classification Computer Science Computer Science Information Systems eeg signal processing Engineering and Technology |
University: | Anna University |
Completed Date: | 2022 |
Abstract: | An efficient processing approach is essential for increasing identification accuracy since the Electroencephalogram (EEG) signals produced by the Brian-Computer Interface (BCI) apparatus are non-linear, non-stationary, and time varying. The interpretation of scalp EEG recordings can be hampered by non-brain contributions to Electroencephalographic (EEG) signals, referred to as artifacts. This is particularly accurate when the artifacts have significant amplitudes such as movement artifacts or appear repeatedly like eye-movement artifacts. Common disturbances in the capture of EEG signals include Electrooculogram (EOG), Electrocardiogram (ECG), Electromyogram (EMG) and other artifacts, which have a significant impact on the extraction of meaningful information. This study suggests integrating the Singular Spectrum Analysis (SSA) and Independent Component Analysis (ICA) methods to pre-process the EEG data. In this research, Higher Order Linear Moment based SSA (HOL-SSA) is used to first decompose the EEG signals into multivariate signals after which the source signals are extracted from the multivariate data using Online Recursive ICA (ORICA). Thus, the proposed HOL-SSA and ORICA based pre-processing approach has shown improved results in artifact rejection. The experimental findings demonstrate that the suggested technique can identify and eliminate EOG, ECG, EMG and other artifacts from EEG data while still preserving brain activity that is ignored by the noise component. The characteristics of the denoised EEG data are then extracted using the Common Spatial Pattern (CSP) technique. newline |
Pagination: | xxiii,138p. |
URI: | http://hdl.handle.net/10603/519603 |
Appears in Departments: | Faculty of Information and Communication Engineering |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 29.08 kB | Adobe PDF | View/Open |
02_prelim pages.pdf | 4.82 MB | Adobe PDF | View/Open | |
03_content.pdf | 23.1 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 16.75 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 189.4 kB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 146.13 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 2.37 MB | Adobe PDF | View/Open | |
08_annexures.pdf | 126.62 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 54.97 kB | Adobe PDF | View/Open |
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