The classification of EEG-based winking signals: a transfer learning and random forest pipeline
Brain Computer-Interface (BCI) technology plays a considerable role in the control of rehabilitation or peripheral devices for stroke patients. This is particularly due to their inability to control such devices from their inherent physical limitations after such an attack. More often than not, the...
Main Authors: | Jothi Letchumy, Mahendra Kumar, Rashid, Mamunur, Rabiu Muazu, Musa, Mohd Azraai, Mohd Razman, Norizam, Sulaiman, Rozita, Jailani, Anwar, P. P. Abdul Majeed |
---|---|
Format: | Article |
Language: | English |
Published: |
PeerJ Inc.
2021
|
Subjects: | |
Online Access: | http://umpir.ump.edu.my/id/eprint/31406/1/The%20classification%20of%20EEG-based%20winking%20signals.pdf |
Similar Items
-
The classification of EEG-based wink signals: A CWT-Transfer Learning pipeline
by: Jothi Letchumy, Mahendra Kumar, et al.
Published: (2021) -
An evaluation of different fast fourier transform - transfer learning pipelines for the classification of wink-based EEG signals
by: Jothi Letchumy, Mahendra Kumar, et al.
Published: (2020) -
The Classification of Wink-Based EEG Signals: The Identification of Significant Time-Domain Features
by: Jothi Letchumy, Mahendra Kumar, et al.
Published: (2021) -
The Classification of Wink-Based EEG Signals: The identification on efficiency of transfer learning models by means of kNN classifier
by: Jothi Letchumy, Mahendra Kumar, et al.
Published: (2021) -
The classification of wink-based eeg signals by means of transfer learning models
by: Jothi Letchumy, Mahendra Kumar
Published: (2021)