brain-computer interface

Design a Novel BCI for Neurorehabilitation Using Concurrent LFP and EEG Features: A Case Study
Author(s)3: Yi Sun, Yu Qi, Yueming Wang, Cuntai Guan, Yu Sun
Design a Novel BCI for Neurorehabilitation Using Concurrent LFP and EEG Features: A Case Study IEEE Transactions on Biomedical Engineering (TBME)
This work introduced for the first time a novel BCI that incorporate both intracortical LFP and scalp EEG (named, LFP-EEG-BCI) for motor intention decoding during neurorehabilitation. Concurrent intracortical and scalp signals were collected from a paraplegic patient undergoing motor imagery (MI) neurorehabilitation training. A common spatial filter approach was adopted for feature extraction and a decision fusion strategy was further introduced to obtain the decoding results. Transfer learning approach was also utilized to reduce the calibration. The proposed novel LFP-EEG-BCI may lead to new directions for developing practical neurorehabilitation systems in clinical applications. read more

Align and Pool for EEG Headset Domain Adaptation (ALPHA) to Facilitate Dry Electrode Based SSVEP-BCI
Author(s)3: Bingchuan Liu, Xiaogang Chen, Xiang Li, Yijun Wang, Xiaorong Gao, Shangkai Gao
Align and Pool for EEG Headset Domain Adaptation (ALPHA) to Facilitate Dry Electrode Based SSVEP-BCI 170 177 IEEE Transactions on Biomedical Engineering (TBME)
This study leverages transfer learning to improve the performance for steady-state visual evoked potential based brain-computer interface (SSVEP-BCI) implemented by dry electrodes. We utilize auxiliary individual electroencephalogram (EEG) recorded from wet electrode for cross-device transfer learning via the proposed framework named ALign and Pool for EEG Headset domain Adaptation (ALPHA), which aligns the SSVEP features by domain adaptation. ALPHA significantly outperformed the competing methods in two transfer directions, and boosted the dry-electrode systems using wet-electrode EEG. The cross-device transfer learning by ALPHA could increase the utility and potentially promote the use of dry electrode based SSVEP-BCIs in practical applications. read more

Fast EEG-based decoding of the directional focus of auditory attention using common spatial patterns
Author(s)3: Simon Geirnaert, Tom Francart, Alexander Bertrand
Fast EEG-based decoding of the directional focus of auditory attention using common spatial patterns 170 177 IEEE Transactions on Biomedical Engineering (TBME)
Current hearing devices lack information about the sound source a user attends to when there are multiple speakers. Auditory attention decoding (AAD) algorithms, which decode the auditory attention from brain signals, solve this problem and inform the hearing device about the to-be-enhanced speaker. While current AAD algorithms typically require an EEG buffer of 10s, leading to long delays, we present a new fast and accurate AAD algorithm that decodes the spatial focus of auditory attention in 1s using common spatial pattern filtering. read more

Brain-Computer Interface-based Soft Robotic Glove Rehabilitation for Stroke
Author(s)3: Nicholas Cheng, Kok Soon Phua, Hwa Sen Lai, Pui Kit Tam, Ka Yin Tang, Kai Kei Cheng, Raye Chen-Hua Yeow, Kai Keng Ang, Jeong Hoon Lim
Brain-Computer Interface-based Soft Robotic Glove Rehabilitation for Stroke 170 177 IEEE Transactions on Biomedical Engineering (TBME)
This paper presents the results of a study involving the use of a Brain-Computer Interface-based Soft Robotic Glove as a novel strategy in stroke rehabilitation. The technology uses the electroencephalogram signals from stroke patients to drive the assistive actions of the soft robotic glove to assist them in physically carrying out activities of daily living. The two-arm study showed prolonged improvements in FMA and ARAT scores although no significant intergroup differences were observed during the study. In addition, all of the patients in the BCI-SRG group also experienced a vivid kinesthetic illusion lasting beyond the active intervention period. read more
3-Dimensional Brain-Computer Interface Control through Simultaneous Overt Spatial Attentional and Motor Imagery Tasks

Three-Dimensional Brain-Computer Interface Control through Simultaneous Overt Spatial Attentional and Motor Imagery Tasks
Author(s)3: Jianjun Meng, Taylor Streitz, Nicholas Gulachek, Daniel Suma, Bin He
Three-Dimensional Brain-Computer Interface Control through Simultaneous Overt Spatial Attentional and Motor Imagery Tasks 170 177 IEEE Transactions on Biomedical Engineering (TBME)

Brain-computer interfacing (BCI) is a promising method for providing alternative connections between the brain and the outside world in concert with natural connections or re-establishing natural limb movement in cases…

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Decoding Covert Somatosensory Attention by a BCI System Calibrated With Tactile Sensation

Decoding Covert Somatosensory Attention by a BCI System Calibrated With Tactile Sensation
Author(s)3: Lin Yao, Xinjun Sheng, Natalie Mrachacz-Kersting, Xiangyang Zhu, Dario Farina, Ning Jiang
Decoding Covert Somatosensory Attention by a BCI System Calibrated With Tactile Sensation 170 177 IEEE Transactions on Biomedical Engineering (TBME)

Objective: We propose a novel calibration strategy to facilitate the decoding of covert somatosensory attention by exploring the oscillatory dynamics induced by tactile sensation. Methods: It was hypothesized that the…

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Feasibility of Automatic Error Detect-and-Undo System in Human Intracortical Brain-Computer Interfaces

Feasibility of Automatic Error Detect-and-Undo System in Human Intracortical Brain-Computer Interfaces
Author(s)3: Nir Evan-Chen, Sergey D. Stavisky, Chethan Pandarinath, Paul Nuyujukian, Chrstine H. Blabe, Leigh R. Hochberg, Jaimie M. Henderson, Krishna V. Shenoy
Feasibility of Automatic Error Detect-and-Undo System in Human Intracortical Brain-Computer Interfaces 170 177 IEEE Transactions on Biomedical Engineering (TBME)

Brain-computer interfaces (BCIs) aim to help people with paralysis to improve their communication and independence. Intracortical BCIs (iBCIs) have shown promising results in pilot clinical trials. Despite the performance improvements…

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A High-Performance Neural Prosthesis Incorporating Discrete State Selection with Hidden Markov Models

A High-Performance Neural Prosthesis Incorporating Discrete State Selection with Hidden Markov Models
Author(s)3: Jonathan C. Kao, Paul Nuyujukian, Stephen I. Ryu, Krishna V. Shenoy
A High-Performance Neural Prosthesis Incorporating Discrete State Selection with Hidden Markov Models 170 177 IEEE Transactions on Biomedical Engineering (TBME)

Communication neural prostheses aim to restore efficient communication to people with paralysis and ALS.  These systems record neural signals from the brain and translate them, through a decoder algorithm, into…

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EEG Source Imaging Enhances the Decoding of Complex Right Hand Motor Imagery Tasks
Author(s)3: Bradley J. Edelman, Bryan Baxter, Bin He
EEG Source Imaging Enhances the Decoding of Complex Right Hand Motor Imagery Tasks 170 177 IEEE Transactions on Biomedical Engineering (TBME)

Bradley J. Edelman, Bryan Baxter, Bin He, University of Minnesota, USA Brain-computer interfaces (BCIs) based on sensorimotor rhythms (SMRs) have achieved successful control of real and virtual devices in up to…

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Separable Common Spatio-Spectral Patterns for Motor Imagery BCI Systems
Author(s)3: Amirhossein S. Aghaei, Mohammad Shahin Mahanta, Konstantinos N. Plataniotis
Separable Common Spatio-Spectral Patterns for Motor Imagery BCI Systems 170 177 IEEE Transactions on Biomedical Engineering (TBME)

Amirhossein S. Aghaei, Mohammad Shahin Mahanta, Konstantinos N. Plataniotis, University of Toronto Brain-Computer Interface (BCI) systems aim to provide a non-muscular channel for the brain to control external devices using electrical…

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