Motor Imagery Classification using local region CSP features with high-gamma band

Neural oscillatory synchrony could implement grouping processes, act as an attentional filter, or foster the storage of information in short-term memory. Do these findings indicate that oscillatory synchrony is an unspecific epiphenomenon occurring in any demanding task, or that oscillatory synchrony is a fundamental mechanism involved whenever neural cooperation is requested? If the latter hypothesis is true, then oscillatory synchrony should be specific, with distinct visual processes eliciting different types of oscillations. We recorded magnetoencephalogram (MEG) signals while manipulating the grouping properties of a visual display on the one hand, and the focusing of attention to memorize part of this display on the other hand. Grouping-related gamma oscillations were present in all conditions but modulated by the grouping properties of the stimulus (one or two groups) in the high gamma-band (70–120 Hz) at central occipital locations. Attention-related gamma oscillations appeared as an additional component whenever attentional focusing was requested in the low gamma-band (44–66 Hz) at parietal locations. Our results thus reveal the existence of a functional specialization in the gamma range, with grouping-related oscillations showing up at higher frequencies than attention-related oscillations. The pattern of oscillatory synchrony is thus specific of the visual process it is associated with. Our results further suggest that both grouping processes and focused attention rely on a common implementation process, namely, gamma-band oscillatory synchrony, a finding that could account for the fact that coherent percepts are more likely to catch attention than incoherent ones.

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TUO05 High-gamma band activity of primary hand cortical areas: a sensorimotor feedback efficiency index

Clinical Neurophysiology ◽

10.1016/s1388-2457(08)60054-1 ◽

2008 ◽

Vol 119 ◽

pp. S13

Author(s):

Franca Tecchio ◽

Filippo Zappasodi ◽

Camillo Porcaro ◽

Giulia Barbati ◽

Giovanni Assenza ◽

...

Keyword(s):

Efficiency Index ◽

Gamma Band ◽

Cortical Areas ◽

High Gamma ◽

Gamma Band Activity ◽

Band Activity ◽

Sensorimotor Feedback

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Distinct neurophysiological correlates of the fMRI BOLD signal in the hippocampus and neocortex

10.1101/2021.02.01.429258 ◽

2021 ◽

Author(s):

Paul F. Hill ◽

Sarah E. Seger ◽

Hye Bin Yoo ◽

Danielle R. King ◽

Bradley C. Lega ◽

...

Keyword(s):

Neural Activity ◽

Blood Oxygen Level Dependent ◽

Gamma Band ◽

Bold Signal ◽

Blood Oxygen Level ◽

Indirect Measure ◽

Neural Architecture ◽

High Gamma ◽

Gamma Band Activity ◽

Band Activity

AbstractFunctional magnetic resonance imaging (fMRI) is among the foremost methods for mapping human brain function but provides only an indirect measure of underlying neural activity. Recent findings suggest that the neurophysiological correlates of the fMRI blood-oxygen-level-dependent (BOLD) signal might be regionally specific. We examined the neurophysiological correlates of the fMRI BOLD signal in the hippocampus and neocortex, where differences in neural architecture might result in a different relationship between the respective signals. Fifteen human neurosurgical patients (10 female, 5 male) implanted with depth electrodes performed a verbal free recall task while electrophysiological activity was recorded simultaneously from hippocampal and neocortical sites. The same patients subsequently performed a similar version of the task during a later fMRI session. Subsequent memory effects (SMEs) were computed for both imaging modalities as patterns of encoding-related brain activity predictive of later free recall. Linear mixed-effects modelling revealed that the relationship between BOLD and gamma-band SMEs was moderated by the lobar location of the recording site. BOLD and high gamma (70-150 Hz) SMEs positively covaried across much of the neocortex. This relationship was reversed in the hippocampus, where a negative correlation between BOLD and high gamma SMEs was evident. We also observed a negative relationship between BOLD and low gamma (30-70 Hz) SMEs in the medial temporal lobe more broadly. These results suggest that the neurophysiological correlates of the BOLD signal in the hippocampus differ from those observed in the neocortex.Significance StatementThe blood-oxygen-level-dependent (BOLD) signal forms the basis of fMRI but provides only an indirect measure of neural activity. Task-related modulation of BOLD signals are typically equated with changes in gamma-band activity; however, relevant empirical evidence comes largely from the neocortex. We examined neurophysiological correlates of the BOLD signal in the hippocampus, where the differing neural architecture might result in a different relationship between the respective signals. We identified a positive relationship between encoding-related changes in BOLD and gamma-band activity in frontal, temporal, and parietal cortex. This effect was reversed in the hippocampus, where BOLD and gamma-band effects negatively covaried. These results suggest regional variability in the transfer function between neural activity and the BOLD signal in the hippocampus and neocortex.

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A Multibranch of Convolutional Neural Network Models for Electroencephalogram-Based Motor Imagery Classification

Biosensors ◽

10.3390/bios12010022 ◽

2022 ◽

Vol 12 (1) ◽

pp. 22

Author(s):

Ghadir Ali Altuwaijri ◽

Ghulam Muhammad

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Motor Imagery ◽

Classification Accuracy ◽

Network Models ◽

Classification Methods ◽

Neural Network Models ◽

High Gamma ◽

Public Datasets ◽

High Level

Automatic high-level feature extraction has become a possibility with the advancement of deep learning, and it has been used to optimize efficiency. Recently, classification methods for convolutional neural network (CNN)-based electroencephalography (EEG) motor imagery have been proposed, and have achieved reasonably high classification accuracy. These approaches, however, use the CNN single convolution scale, whereas the best convolution scale varies from subject to subject. This limits the precision of classification. This paper proposes multibranch CNN models to address this issue by effectively extracting the spatial and temporal features from raw EEG data, where the branches correspond to different filter kernel sizes. The proposed method’s promising performance is demonstrated by experimental results on two public datasets, the BCI Competition IV 2a dataset and the High Gamma Dataset (HGD). The results of the technique show a 9.61% improvement in the classification accuracy of multibranch EEGNet (MBEEGNet) from the fixed one-branch EEGNet model, and 2.95% from the variable EEGNet model. In addition, the multibranch ShallowConvNet (MBShallowConvNet) improved the accuracy of a single-scale network by 6.84%. The proposed models outperformed other state-of-the-art EEG motor imagery classification methods.

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