Name
Abstract | ||
|---|---|---|
Brain-computer interface (BCI) systems have recently emerged as a prominent technology for assisting paralyzed people. Recovery from paralysis in most patients using the existing BCI-based assistive devices is hindered due to the lack of training and proper supervision. The system's continuous usage results in mental fatigue, owing to a higher user concentration required to execute the mental commands. Moreover, the false-positive rate and lack of constant control of the BCI systems result in user frustration. In this paper, we propose a framework for BCI systems that utilize deep learning (DL) in a efficient manner to reduce mental fatigue and frustration. The proposed Deep learning Brain System (DBS) recognizes the patient's intention for upper limb movement by a DL model based on the features extracted during training. DBS correlates and maps the different electroencephalogram (EEG) patterns of healthy subjects with the identified pattern's upper limb movement. The stroke-affected muscles of the paralyzed are then activated using the obtained superior pattern. The implemented DBS consisting of four healthy subjects and a quadriplegic patient achieved 94% accuracy for various patient movement intentions. The results show that DBS is an excellent tool for providing rehabilitation, and it delivers sustained assistance, even in the absence of caregivers. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.compeleceng.2021.107113 | COMPUTERS & ELECTRICAL ENGINEERING |
Keywords | DocType | Volume |
BCI, DBS, Deep learning, EEG, Intelligent system, Rehabilitation | Journal | 92 |
ISSN | Citations | PageRank |
0045-7906 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| P G Vinoj | 1 | 0 | 0.34 |
| Sunil Jacob | 2 | 14 | 3.61 |
| Varun G. Menon | 3 | 70 | 15.66 |
| Venki Balasubramanian | 4 | 0 | 0.34 |
| Md. Jalil Piran | 5 | 0 | 0.68 |