Phase resetting and evoked activity contribute to the genesis of P300 signal in BCI system

AbstractIntegration of information across the senses is critical for perception and is a common property of neurons in the cerebral cortex, where it is thought to arise primarily from corticocortical connections. Much less is known about the role of subcortical circuits in shaping the multisensory properties of cortical neurons. We show that stimulation of the whiskers causes widespread suppression of sound-evoked activity in mouse primary auditory cortex (A1). This suppression depends on the primary somatosensory cortex (S1), and is implemented through a descending circuit that links S1, via the auditory midbrain, with thalamic neurons that project to A1. Furthermore, a direct pathway from S1 has a facilitatory effect on auditory responses in higher-order thalamic nuclei that project to other brain areas. Crossmodal corticofugal projections to the auditory midbrain and thalamus therefore play a pivotal role in integrating multisensory signals and in enabling communication between different sensory cortical areas.

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Phase resetting in central and peripheral sinoatrial node cell models

Computers in Cardiology, 2004 ◽

10.1109/cic.2004.1442985 ◽

2005 ◽

Author(s):

D.G. Tsalikakis ◽

H.G. Zhang ◽

D.I. Fotiadis ◽

G.P. Kremmydas

Keyword(s):

Sinoatrial Node ◽

Phase Resetting ◽

Cell Models ◽

Sinoatrial Node Cell

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Pulse Inputs Affect Timings of Spikes in Neurons with or Without Time Delays

International Journal of Nonlinear Sciences and Numerical Simulation ◽

10.1515/ijnsns-2017-0070 ◽

2019 ◽

Vol 20 (3-4) ◽

pp. 257-267

Author(s):

Jiaoyan Wang ◽

Xiaoshan Zhao ◽

Chao Lei

Keyword(s):

Time Delays ◽

Single Neuron ◽

Spiking Neurons ◽

Phase Resetting ◽

Subthreshold Oscillations ◽

Weak Pulse

AbstractInputs can change timings of spikes in neurons. But it is still not clear how input’s parameters for example injecting time of inputs affect timings of neurons. HR neurons receiving both weak and strong inputs are considered. How pulse inputs affecting neurons is studied by using the phase-resetting curve technique. For a single neuron, weak pulse inputs may advance or delay the next spike, while strong pulse inputs may induce subthreshold oscillations depending on parameters such as injecting timings of inputs. The behavior of synchronization in a network with or without coupling delays can be predicted by analysis in a single neuron. Our results can be used to predict the effects of inputs on other spiking neurons.

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The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning

Sensors ◽

10.3390/s21051613 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1613

Author(s):

Man Li ◽

Feng Li ◽

Jiahui Pan ◽

Dengyong Zhang ◽

Suna Zhao ◽

...

Keyword(s):

Brain Computer Interface ◽

Computer Interface ◽

System Control ◽

Average Accuracy ◽

Training Samples ◽

The Disabled ◽

Limited Training Samples ◽

The Stability ◽

Electroencephalogram Eeg ◽

Bci System

In addition to helping develop products that aid the disabled, brain–computer interface (BCI) technology can also become a modality of entertainment for all people. However, most BCI games cannot be widely promoted due to the poor control performance or because they easily cause fatigue. In this paper, we propose a P300 brain–computer-interface game (MindGomoku) to explore a feasible and natural way to play games by using electroencephalogram (EEG) signals in a practical environment. The novelty of this research is reflected in integrating the characteristics of game rules and the BCI system when designing BCI games and paradigms. Moreover, a simplified Bayesian convolutional neural network (SBCNN) algorithm is introduced to achieve high accuracy on limited training samples. To prove the reliability of the proposed algorithm and system control, 10 subjects were selected to participate in two online control experiments. The experimental results showed that all subjects successfully completed the game control with an average accuracy of 90.7% and played the MindGomoku an average of more than 11 min. These findings fully demonstrate the stability and effectiveness of the proposed system. This BCI system not only provides a form of entertainment for users, particularly the disabled, but also provides more possibilities for games.

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A robust and reliable online P300-based BCI system using Emotiv EPOC + headset

Journal of Medical Engineering & Technology ◽

10.1080/03091902.2020.1853840 ◽

2021 ◽

pp. 1-19

Author(s):

Islam A. Fouad

Keyword(s):

Bci System

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Dynamic relationships between spontaneous and evoked electrophysiological activity

Communications Biology ◽

10.1038/s42003-021-02240-9 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Soren Wainio-Theberge ◽

Annemarie Wolff ◽

Georg Northoff

Keyword(s):

Neural Activity ◽

Large Scale ◽

Behavioral Outcomes ◽

Low Frequencies ◽

Scale Free ◽

Non Invasive ◽

Spontaneous Neural Activity ◽

Electrophysiological Signals ◽

Evoked Activity ◽

Dynamic Relationships

AbstractSpontaneous neural activity fluctuations have been shown to influence trial-by-trial variation in perceptual, cognitive, and behavioral outcomes. However, the complex electrophysiological mechanisms by which these fluctuations shape stimulus-evoked neural activity remain largely to be explored. Employing a large-scale magnetoencephalographic dataset and an electroencephalographic replication dataset, we investigate the relationship between spontaneous and evoked neural activity across a range of electrophysiological variables. We observe that for high-frequency activity, high pre-stimulus amplitudes lead to greater evoked desynchronization, while for low frequencies, high pre-stimulus amplitudes induce larger degrees of event-related synchronization. We further decompose electrophysiological power into oscillatory and scale-free components, demonstrating different patterns of spontaneous-evoked correlation for each component. Finally, we find correlations between spontaneous and evoked time-domain electrophysiological signals. Overall, we demonstrate that the dynamics of multiple electrophysiological variables exhibit distinct relationships between their spontaneous and evoked activity, a result which carries implications for experimental design and analysis in non-invasive electrophysiology.

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Induction of Neural Plasticity Using a Low-Cost Open Source Brain-Computer Interface and a 3D-Printed Wrist Exoskeleton

Sensors ◽

10.3390/s21020572 ◽

2021 ◽

Vol 21 (2) ◽

pp. 572

Author(s):

Mads Jochumsen ◽

Taha Al Muhammadee Janjua ◽

Juan Carlos Arceo ◽

Jimmy Lauber ◽

Emilie Simoneau Buessinger ◽

...

Keyword(s):

Open Source ◽

Neural Plasticity ◽

Motor Evoked Potentials ◽

Low Cost ◽

True Positive Rate ◽

Positive Rate ◽

3D Printed ◽

Major Factors ◽

And Control ◽

Bci System

Brain-computer interfaces (BCIs) have been proven to be useful for stroke rehabilitation, but there are a number of factors that impede the use of this technology in rehabilitation clinics and in home-use, the major factors including the usability and costs of the BCI system. The aims of this study were to develop a cheap 3D-printed wrist exoskeleton that can be controlled by a cheap open source BCI (OpenViBE), and to determine if training with such a setup could induce neural plasticity. Eleven healthy volunteers imagined wrist extensions, which were detected from single-trial electroencephalography (EEG), and in response to this, the wrist exoskeleton replicated the intended movement. Motor-evoked potentials (MEPs) elicited using transcranial magnetic stimulation were measured before, immediately after, and 30 min after BCI training with the exoskeleton. The BCI system had a true positive rate of 86 ± 12% with 1.20 ± 0.57 false detections per minute. Compared to the measurement before the BCI training, the MEPs increased by 35 ± 60% immediately after and 67 ± 60% 30 min after the BCI training. There was no association between the BCI performance and the induction of plasticity. In conclusion, it is possible to detect imaginary movements using an open-source BCI setup and control a cheap 3D-printed exoskeleton that when combined with the BCI can induce neural plasticity. These findings may promote the availability of BCI technology for rehabilitation clinics and home-use. However, the usability must be improved, and further tests are needed with stroke patients.

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