A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics (original) (raw)

An Integrated Machine Learning-Based Brain Computer Interface to Classify Diverse Limb Motor Tasks: Explainable Model

Sensors

The Berlin Brain-Computer Interface: Machine Learning Based Detection of User Specific Brain States

J. Univers. Comput. Sci., 2006

A Characterization of Brain-Computer Interface Performance Trade-Offs Using Support Vector Machines and Deep Neural Networks to Decode Movement Intent

Marcie Bockbrader

Frontiers in Neuroscience, 2018

View PDFchevron_right

In search of more robust decoding algorithms for neural prostheses, a data driven approach

Erk Subasi

2010

View PDFchevron_right

Combined Analysis of Cortical (EEG) and Nerve Stump Signals Improves Robotic Hand Control

Mario Tombini

Neurorehabilitation and Neural Repair, 2011

View PDFchevron_right

Motion prediction for the sensorimotor control of hand prostheses with a brain-machine interface using EEG

Selim Eskiizmirliler

2022 10th International Winter Conference on Brain-Computer Interface (BCI), 2022

View PDFchevron_right

Connectivity analysis as a novel approach to motor decoding for prosthesis control

Anastasios Bezerianos

IEEE Transactions on Biomedical Engineering, 2000

View PDFchevron_right

An empirical comparison of neural networks and machine learning algorithms for EEG gait decoding

Jose Contreras-vidal

Scientific Reports, 2020

View PDFchevron_right

Classification of Electroencephalogram Data from Hand Grasp and Release Movements for BCI Controlled Prosthesis

Khairunnisa Johar

Procedia Technology, 2016

View PDFchevron_right

Dynamic Fusion of Electromyographic and Electroencephalographic Data towards Use in Robotic Prosthesis Control

Abraham Itzhak Weinberg

Journal of Physics: Conference Series, 2021

View PDFchevron_right

Classification of different reaching movements from the same limb using EEG

Ander Ramos-murguialday

Journal of Neural Engineering, 2017

View PDFchevron_right

Restoration of Whole Body Movement: Toward a Noninvasive Brain-Machine Interface System

Jose Contreras-Vidal

IEEE Pulse, 2000

View PDFchevron_right

Machine learning for electroencephalography decoding and robotics dextrous hands movement

Prof. Ebrahim A . Mattar

2018

View PDFchevron_right

Towards Improved EEG Interpretation for the Control of a Prosthetic/Orthotic Hand using a BCI

Tshilidzi Marwala

View PDFchevron_right

Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface

Marcie Bockbrader

View PDFchevron_right

A bioelectric neural interface towards intuitive prosthetic control for amputees

Cynthia Overstreet

Journal of Neural Engineering, 2020

View PDFchevron_right

Investigating Upper Limb Movement Classification on Users with Tetraplegia as a Possible Neuroprosthesis Interface

David Guiraud

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018

View PDFchevron_right

Categorizing Imagined Right and Left Motor Imagery BCI Tasks for Low-cost Robotic Neuroprosthesis

Sandeep Bodda, Pooja Viswam, Swathy Krishna M, Harikrishnan Chandranpillai, Shyam Diwakar

View PDFchevron_right

The Brain–Computer Interface: Experience of Construction, Use, and Potential Routes to Improving Performance

Ksenia Volkova

Neuroscience and Behavioral Physiology, 2018

View PDFchevron_right

Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users

Maryam Alimardani

PLOS ONE

View PDFchevron_right

Data augmentation strategies for EEG-based motor imagery decoding

Olawunmi George

Heliyon

View PDFchevron_right

Brain Computer Interfaces Employing Machine Learning Methods : A Systematic Review

sejal vyas

International Journal of Computer Science & Engineering Survey, 2020

View PDFchevron_right

A Survey of EEG and Machine Learning-Based Methods for Neural Rehabilitation

Rupali Gill

IEEE Access

View PDFchevron_right

Editorial: Sensorimotor decoding: characterization and modeling for rehabilitation and assistive technologies

Miguel Pais-Vieira

Frontiers in Human Neuroscience

View PDFchevron_right

Toward Electrocorticographic Control of a Dexterous Upper Limb Prosthesis: Building Brain-Machine Interfaces

Nitish Thakor

IEEE Pulse, 2012

View PDFchevron_right

EEG-Based Lower-Limb Movement Onset Decoding: Continuous Classification and Asynchronous Detection

Mohamed Bouri

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2018

View PDFchevron_right

Gumpy: a Python toolbox suitable for hybrid brain–computer interfaces

Nicolai Waniek

Journal of Neural Engineering

View PDFchevron_right

Brain–computer interface in paralysis

Leonardo Cohen

Current Opinion in Neurology, 2008

View PDFchevron_right

Classifying EEG and ECoG Signals Without Subject Training for Fast BCI Implementation: Comparison of Nonparalyzed and Completely Paralyzed Subjects

Michael Tangermann

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006

View PDFchevron_right

Improving cross-subject classification performance of motor imagery signals: a data augmentation-focused deep learning framework

Elif Tülay, Enes Ozelbas

Machine Learning: Science and Technology, 2024

View PDFchevron_right

Towards Improved EEG Interpretation for the Control of a Prosthetic / Orthotic Hand using a BCI Single-trial right and left hand discrimination

Abdul-Khaaliq Mohamed

2012

View PDFchevron_right

A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics (original) (raw)

Related papers