A Review of Motor Brain-Computer Interfaces using Intracranial Electroencephalography based on Surface Electrodes and Depth Electrodes

X Wu, B Metcalfe, S He, H Tan… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Brain-computer interfaces (BCIs) provide a communication interface between the brain and
external devices and have the potential to restore communication and control in patients …

Electrocorticography based motor imagery movements classification using long short-term memory (LSTM) based on deep learning approach

M Rashid, M Islam, N Sulaiman, BS Bari, RK Saha… - SN Applied …, 2020 - Springer
Brain–computer interface (BCI) is an important alternative for disabled people that enables
the innovative communication pathway among individual thoughts and different assistive …

Amputee walking mode recognition based on mel frequency cepstral coefficients using surface electromyography sensor

T Hussain, N Iqbal, HF Maqbool… - … Journal of Sensor …, 2020 - inderscienceonline.com
Walking mode recognition through surface electromyography (sEMG) sensors is an active
field of smart prostheses technologies. This work presents the mel frequency cepstral …

Empirical mode decomposition coupled with fast fourier transform based feature extraction method for motor imagery tasks classification

MN Islam, N Sulaiman, M Rashid… - 2020 IEEE 10th …, 2020 - ieeexplore.ieee.org
Brain-Computer Interfaces (BCI) offers a robust solution to the people with disabilities and
allows for creative connectivity between the user's intention and supporting tools. Different …

Feature engineering for an efficient motor related ecog bci system

R Jain, P Jaiman, V Baths - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
Invasive Brain Computer Interface (BCI) systems through Electrocorticographic (ECoG)
signals require efficient recognition of spatiotemporal patterns from a multi-electrodes …

Improving the Classifier Performance in Motor Imagery Task Classification: What are the steps in the classification process that we should worry about?

MS Santos, PH Abreu, G Rodriguez-Bermudez… - International Journal of …, 2018 - Springer
Abstract Brain-Computer Interface systems based on motor imagery are able to identify an
individual's intent to initiate control through the classification of encephalography patterns …

[PDF][PDF] A Comparative Study of Machine Learning Algorithms for EEG Signal Classification

A Hashmi, BA Khan, O Farooq - Signal & Image Processing: An …, 2021 - academia.edu
In this paper, different machine learning algorithms such as Linear Discriminant Analysis,
Support vector machine (SVM), Multi-layer perceptron, Random forest, K-nearest neighbour …

Feature engineering benchmark for motor related decoding through ECoG signals

R Jain, P Jaiman, V Baths - bioRxiv, 2023 - biorxiv.org
Invasive Brain Computer Interface (BCI) systems through Electrocorticographic (ECoG)
signals require efficient recognition of spatio-temporal patterns from a multi-electrodes …

Vrushali G. Raut, Sanjay R. Ganorkar

SO Rajankar, OS Rajankar - api.taylorfrancis.com
Brain computer interface (BCI) emerging from the past four decades as a specialized
medium that offers the effective control of the environment, which may include devices such …

[PDF][PDF] MOTOR IMAGERY SIGNALS USING MACHINE LEARNING TOOLS

A Hashmi, BA Khan, O Farooq - researchgate.net
In this paper, we propose a system for the purpose of classifying Electroencephalography
(EEG) signals associated with imagined movement of right hand and relaxation state using …