Academic Journal
A combined EEG motor and speech imagery paradigm with automated successive halving for customizable command selection
| Title: | A combined EEG motor and speech imagery paradigm with automated successive halving for customizable command selection |
|---|---|
| Authors: | Natasha Padfield, Tracey Camilleri, Simon Fabri, Marvin Bugeja, Kenneth Camilleri |
| Source: | Brain-Computer Interfaces. 11:125-142 |
| Publisher Information: | Informa UK Limited, 2024. |
| Publication Year: | 2024 |
| Subject Terms: | 03 medical and health sciences, Support vector machines, 0302 clinical medicine, Signal processing -- Digital techniques, Speech processing systems -- Evaluation, 0202 electrical engineering, electronic engineering, information engineering, Electroencephalography, 02 engineering and technology, Brain-computer interfaces |
| Description: | The classification performance of endogenous electroencephalogram (EEG) brain-computer interfaces (BCIs) can be improved by hybridizing the paradigm through the use of commands from multiple paradigms. Hybrid paradigms using motor imagery (MI) and speech imagery (SI) have shown promise, but there is a lack of research into: i) their effectiveness when compared to pure MI and SI for multiclass problems, and ii) automated command selection. This study investigates multiclass MI and SI hybrid paradigms and compares the results to those obtained using pure MI and SI. Performance was assessed using F1 score and accuracy. The performances of all possible hybrid paradigm designs were assessed. The analysis indicated that hybridization does not always guarantee improved performance when compared to the pure paradigms, and there is inter-subject variation in the best paradigm. This confirmed the need for automated subject-specific hybrid paradigm designs. An automated hybrid paradigm selection technique using successive halving (SH) for expedited computational times was developed and results were compared to those obtained using a standard grid search. The SH approach resulted in an improvement in F1 score of 21.09% and 36.86% compared to MI and SI and led to a reduction in computational times of 82.80% compared to grid search. |
| Document Type: | Article Other literature type |
| Language: | English |
| ISSN: | 2326-2621 2326-263X |
| DOI: | 10.1080/2326263x.2024.2379009 |
| DOI: | 10.6084/m9.figshare.26346574.v1 |
| DOI: | 10.6084/m9.figshare.26346574 |
| Access URL: | https://www.um.edu.mt/library/oar/handle/123456789/124896 |
| Rights: | CC BY NC ND CC BY |
| Accession Number: | edsair.doi.dedup.....b94f64d14f5101bd1fc2084444cc8b63 |
| Database: | OpenAIRE |
| ISSN: | 23262621 2326263X |
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| DOI: | 10.1080/2326263x.2024.2379009 |