IEEE Transactions on Biomedical Engineering

Featured Articles
Design a Novel BCI for Neurorehabilitation Using Concurrent LFP and EEG Features: A Case Study
Zhao Feng, Yi Sun, Linze Qian, Yu Qi, Yueming Wang, Cuntai Guan, Yu Sun
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
Posted on 29 APR 2022
Articles
A Temporal-Spectral-based Squeeze-and-Excitation Feature Fusion Network for Motor Imagery EEG Decoding
Fangang Meng, Jingyu Liu, Lianghui Guo, Yang Li, Yu Liu
Motor imagery (MI) electroencephalography (EEG) decoding plays an important role in brain-computer interface (BCI), which enables motor-disabled patients to communicate with the outside world via external devices. Recent deep learning methods, which fail to fully explore both deep-temporal characterizations in... Read more
Posted on 28 JUL 2021
Featured Articles
Decoding Finger Tapping with the Affected Hand in Chronic Stroke Patients During Motor Imagery and Execution
Minji Lee, Ji-Hoon Jeong, Yun-Hee Kim, Seong-Whan Lee
In stroke rehabilitation, motor imagery based on a brain–computer interface is an extremely useful method to control an external device and utilize neurofeedback. Many studies have reported on the classification performance of motor imagery to decode individual fingers in stroke... Read more
Posted on 11 JUN 2021
Featured Articles
Brain-Computer Interface-based Soft Robotic Glove Rehabilitation for Stroke
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
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
Posted on 30 NOV 2020
Featured Articles
On Error-Related Potentials During Sensorimotor-Based Brain-Computer Interface: Explorations With a Pseudo-Online Brain-Controlled Speller
Jose del R. Millan, Michele Bevilacqua, Serafeim Perdikis
Brain-computer interface (BCI) spelling is a promising communication solution for people in paralysis. Currently, BCIs suffer from imperfect decoding accuracy which calls for methods to handle spelling mistakes. Detecting error-related potentials (ErrPs) has been early identified as a potential remedy.... Read more
Posted on 27 FEB 2020
Featured Articles
Three-Dimensional Brain-Computer Interface Control through Simultaneous Overt Spatial Attentional and Motor Imagery Tasks
Jianjun Meng, Taylor Streitz, Nicholas Gulachek, Daniel Suma, Bin He
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 where these have been potentially disrupted by disease or injury.... Read more
Posted on 22 OCT 2018
Featured Articles
Android Feedback-based Training modulates Sensorimotor Rhythms during Motor Imagery
Christian I. Penaloza, Maryam Alimardani, Shuichi Nishio
   EEG-based brain computer interface (BCI) systems have demonstrated potential to assist patients with devastating motor paralysis conditions. However, there is great interest in shifting the BCI trend toward applications aimed at healthy users. Although BCI operation depends on technological factors... Read more
Posted on 27 FEB 2018
Featured Articles
Open Access Dataset for EEG+NIRS Single-Trial Classification
Jaeyoung Shin, Alexander von Luhmann, Benjamin Blankertz, Do-Won Kim, Jichai Jeong, Han-Jeong Hwang, Klaus-Robert Müller
       We provide an open access dataset for hybrid brain-computer interfaces (BCIs) using electroencephalography (EEG) and near-infrared spectroscopy (NIRS). For this, we conducted two BCI experiments (left vs. right hand motor imagery; mental arithmetic vs. resting state). The dataset was validated... Read more
Posted on 2 OCT 2017
Featured Articles
A Stimulus-Independent Hybrid BCI Based on Motor Imagery and Somatosensory Attentional Orientation
Lin Yao, Xinjun Sheng, Dingguo Zhang, Ning Jiang, Natalie Mrachacz-Kersting, Xiangyang Zhu, Dario Farina
       Distinctive EEG signals from the motor and somatosensory cortex are generated during mental tasks of motor imagery (MI) and somatosensory attentional orientation (SAO). In this study, we hypothesize that a combination of these two signal modalities provides improvements in BCI... Read more
Posted on 16 AUG 2017
Featured Articles, Special Issue: BRAIN
EEG Source Imaging Enhances the Decoding of Complex Right Hand Motor Imagery Tasks
Bradley J. Edelman, Bryan Baxter, Bin He
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 three dimensions. SMR BCI control signals are founded on the user’s... Read more
Posted on 26 DEC 2015