EEG

Do Interictal Epileptiform Discharges and Brain Responses to Electrical Stimulation Come from the Same Location? An Advanced Source Localization Solution

Do Interictal Epileptiform Discharges and Brain Responses to Electrical Stimulation Come from the Same Location? An Advanced Source Localization Solution
Do Interictal Epileptiform Discharges and Brain Responses to Electrical Stimulation Come from the Same Location? An Advanced Source Localization Solution 789 444 IEEE Transactions on Biomedical Engineering (TBME)
This is the first study to demonstrate that delayed responses to electrical stimulation and interictal epileptiform discharges originate from the same location in the brain. It uses an adaptive Bayesian beamformer that is sensitive to target morphology and robust to possible temporal delay mismatches. read more

Sleep Monitoring Using Ear-Centered Setups: Investigating the Influence From Electrode Configurations
Author(s)3: Mike Lind Rank, Preben Kidmose
Sleep Monitoring Using Ear-Centered Setups: Investigating the Influence From Electrode Configurations IEEE Transactions on Biomedical Engineering (TBME)
We combine ear-EEG sleep recordings with a state-of-the-art sleep scoring model, ‘seqsleepnet’, to investigate the upper limits of mobile sleep scoring. We manage to further improve on the state of the art in this field, and perform a detailed analysis of the influence of electrode positioning. From this, we find a general rule of thumb that as long a data set contain EOG information and electrode distance on the order of the width of the head, then good automatic sleep scoring is possible. We also find indications that the obtained automatic scoring may be more reliable than the manual scoring. 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
Electrophysiological Brain Connectivity: Theory and Implementation

Electrophysiological Brain Connectivity: Theory and Implementation
Author(s)3: Bin He, Laura Astolfi, Pedro Antonio Valdés-Sosa, Daniele Marinazzo, Satu O. Palva, Christian-George Bénar, Christoph M. Michel, Thomas Koenig
Electrophysiological Brain Connectivity: Theory and Implementation 170 177 IEEE Transactions on Biomedical Engineering (TBME)

Brain function and dysfunction are encoded in networks within the brain that are distributed over 3-dimensional space and evolves in time. It is of great importance to image brain activation…

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Is EMG a Viable Alternative to BCI for Detecting Movement Intention in Severe Stroke?
Author(s)3: Sivakumar Balasubramanian, Eliana Garcia-Cossio, Niels Birbaumer, Etienne Burdet, Ander Ramos-Murgialday
Is EMG a Viable Alternative to BCI for Detecting Movement Intention in Severe Stroke? 170 177 IEEE Transactions on Biomedical Engineering (TBME)

EEG-based brain-computer interface (BCI) has been used to detect movement intention in severely affected stroke patients during assisted therapy, but current EEG-BCI systems are not practical for routine clinical use.…

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Electromagnetic Brain Source Imaging by Means of a Robust Minimum Variance Beamformer
Author(s)3: Seyed Amir Hossein Hosseini, Abbas Sohrabpour, Mehmet Akçakaya, Bin He
Electromagnetic Brain Source Imaging by Means of a Robust Minimum Variance Beamformer 170 177 IEEE Transactions on Biomedical Engineering (TBME)

Adaptive beamformer methods have been used extensively for functional brain imaging using EEG/MEG surface recordings. However, the sensitivity of beamformers to model mismatches impedes their widespread application, in practice. In…

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Computationally Efficient Algorithms for Sparse, Dynamic Solutions to the EEG Source Localization Problem
Author(s)3: Elvira Pirondini, Behtash Babadi, Gabriel Obregon-Henao, Camilo Lamus, Wasim Q. Malik, Matti S. Hämäläinen, Patrick L. Purdon
Computationally Efficient Algorithms for Sparse, Dynamic Solutions to the EEG Source Localization Problem 170 177 IEEE Transactions on Biomedical Engineering (TBME)

Electroencephalography (EEG) and magnetoencephalography noninvasively record scalp electromagnetic fields generated by cerebral currents, revealing millisecond-level brain dynamics useful for neuroscience and clinical applications. Estimating the currents that generate these fields,…

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M3BA: A Mobile, Modular, Multimodal Biosignal Acquisition architecture for miniaturized EEG-NIRS based hybrid BCI and monitoring

M3BA: A Mobile, Modular, Multimodal Biosignal Acquisition architecture for miniaturized EEG-NIRS based hybrid BCI and monitoring
Author(s)3: Alexander von Luhmann, Heidrun Wabnitz, Tilmann Sander, Klaus-Robert Müller
M3BA: A Mobile, Modular, Multimodal Biosignal Acquisition architecture for miniaturized EEG-NIRS based hybrid BCI and monitoring 170 177 IEEE Transactions on Biomedical Engineering (TBME)

For the further development of the fields of telemedicine, neurotechnology and Brain-Computer Interfaces (BCI), advances in hybrid multimodal signal acquisition and processing technology are invaluable. Currently, there are no commonly…

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Learning Recurrent Waveforms within EEGs
Author(s)3: Austin J. Brockmeier, Jose C. Principe
Learning Recurrent Waveforms within EEGs 170 177 IEEE Transactions on Biomedical Engineering (TBME)

Austin J. Brockmeier,  Jose C. Principe, University of Liverpool, UK, University of Florida, USA When experts analyze EEGs they look for landmarks in the traces corresponding to established waveform patterns, such as phasic…

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Brain-Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives
Brain-Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives 556 235 IEEE Transactions on Biomedical Engineering (TBME)

Han Yuan, Bin He Laureate Institute for Brain Research, Tulsa, Oklahoma, USA; Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota, USA, Volume 61, Issue 5, Page: 1425 – 1435     …

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