Online multi-class brain-computer interface for detection and classification of lower limb movement intentions and kinetics for stroke rehabilitation

2015 ◽  
Vol 2 (4) ◽  
pp. 202-210 ◽  
Author(s):  
Mads Jochumsen ◽  
Imran Khan Niazi ◽  
Muhammad Samran Navid ◽  
Muhammad Nabeel Anwar ◽  
Dario Farina ◽  
...  
Keyword(s):  
Lower Limb ◽  
Stroke Rehabilitation ◽  
Brain Computer Interface ◽  
Limb Movement ◽  
Computer Interface ◽  
Lower Limb Movement

fMRI-Informed EEG for brain mapping of imagined lower limb movement: Feasibility of a brain computer interface

2021 ◽  
Vol 363 ◽  
pp. 109339
Author(s):  
Adrienne Kline ◽  
Nils D. Forkert ◽  
Banafshe Felfeliyan ◽  
Daniel Pittman ◽  
Bradley Goodyear ◽  
...  
Keyword(s):  
Brain Mapping ◽  
Lower Limb ◽  
Brain Computer Interface ◽  
Limb Movement ◽  
Computer Interface ◽  
Lower Limb Movement

Simultaneous Classification of Multiple Motor Imagery and P300 for Increase in Output Information of Brain-computer Interface

2013 ◽  
Vol 133 (3) ◽  
pp. 635-641
Author(s):  
Genzo Naito ◽  
Lui Yoshida ◽  
Takashi Numata ◽  
Yutaro Ogawa ◽  
Kiyoshi Kotani ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Output Information

Classification of EEG Mental Patterns by Using Two Scalp Electrodes and Mahalanobis Distance-Based Classifiers

2002 ◽  
Vol 41 (04) ◽  
pp. 337-341 ◽  
Author(s):  
F. Cincotti ◽  
D. Mattia ◽  
C. Babiloni ◽  
F. Carducci ◽  
L. Bianchi ◽  
...  
Keyword(s):  
Mahalanobis Distance ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Correct Classification ◽  
Interface Area ◽  
System A ◽  
The Brain ◽  
Full Covariance Matrix ◽  
Recording Electrodes

Summary Objectives: In this paper, we explored the use of quadratic classifiers based on Mahalanobis distance to detect mental EEG patterns from a reduced set of scalp recording electrodes. Methods: Electrodes are placed in scalp centro-parietal zones (C3, P3, C4 and P4 positions of the international 10-20 system). A Mahalanobis distance classifier based on the use of full covariance matrix was used. Results: The quadratic classifier was able to detect EEG activity related to imagination of movement with an affordable accuracy (97% correct classification, on average) by using only C3 and C4 electrodes. Conclusions: Such a result is interesting for the use of Mahalanobis-based classifiers in the brain computer interface area.

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