TY  - GEN
CY  - Kuching, Malaysia
KW  - adaptive
KW  -  automatic
KW  -  ALS
KW  -  BCI
KW  -  EEG
KW  -  motor
imagery
A1  - Patel, R
A1  - Bryson, B
A1  - Jiang, D
A1  - Demosthenous, A
ID  - discovery10205462
N2  - Amyotrophic Lateral Sclerosis (ALS) has been a grossly misrepresented end user group when developing coadaptive algorithms for Brain Computer Interfaces (BCI). Researchers have credited this issue to the difficulty of progressing disease in patients with ALS. This non-stationarity reduces accuracy over time. This paper introduces an online model, usable for a BCI using ALS patients data. The automatic coadaptive model effectively decodes 3 class motor imagery (MI) of the left, right hand and rest while adapting to address non-stationarities of Electroencephalography (EEG) over time caused by various factors over the study duration. Adapting Filter bank Common Spatial Pattern (FBCSP) algorithm, where we show it could enable above 70% detection of hand MI in ALS end users longitudinally, previously lacking evidence. The evaluation results demonstrate that the model achieves average accuracies of 72.6% over a 1-2 month period of usage involving 8 ALS patients. This work shows the first auto-adaptive model with ALS patient EEG data providing a stronger incentive for further investigation by setting benchmark models on longitudinal datasets contributing to the solution of multiple challenges in this field.
SN  - 1062-922X
PB  - IEEE
UR  - https://doi.org/10.1109/smc54092.2024.10832078
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
TI  - Auto-Adaptive Model for Longitudinal Motor Imagery Decoding in Amyotrophic Lateral Sclerosis
EP  - 1433
AV  - public
Y1  - 2025/01/20/
SP  - 1429
ER  -