A Review of Motor Brain-Computer Interfaces using Intracranial Electroencephalography based on Surface Electrodes and Depth Electrodes
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 …
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
Brain–computer interface (BCI) is an important alternative for disabled people that enables
the innovative communication pathway among individual thoughts and different assistive …
the innovative communication pathway among individual thoughts and different assistive …
Amputee walking mode recognition based on mel frequency cepstral coefficients using surface electromyography sensor
Walking mode recognition through surface electromyography (sEMG) sensors is an active
field of smart prostheses technologies. This work presents the mel frequency cepstral …
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
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 …
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 …
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?
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 …
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
In this paper, different machine learning algorithms such as Linear Discriminant Analysis,
Support vector machine (SVM), Multi-layer perceptron, Random forest, K-nearest neighbour …
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 …
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 …
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 …
(EEG) signals associated with imagined movement of right hand and relaxation state using …