A comparison of directed functional connectivity among fist-related brain activities during movement imagery, movement execution, and movement observation

ETCNet: An EEG-based motor imagery classification model combining efficient channel attention and temporal convolutional network

Brain-computer interface (BCI) enables the control of external devices using signals from the brain, offering immense potential in assisting individuals with neuromuscular disabilities. Among the different paradigms of BCI systems, the motor imagery (MI) based electroencephalogram (EEG) signal is widely recognized as exceptionally promising. Deep learning (DL) has found extensive applications in the processing of MI signals, wherein convolutional neural networks (CNN) have demonstrated superior performance compared to conventional machine learning (ML) approaches. Nevertheless, challenges related to subject independence and subject dependence persist, while the inherent low signal-to-noise ratio of EEG signals remains a critical aspect that demands attention. Accurately deciphering intentions from EEG signals continues to present a formidable challenge. This paper introduces an advanced end-to-end network that effectively combines the efficient channel attention (ECA) and temporal convolutional network (TCN) components for the classification of motor imagination signals. We incorporated an ECA module prior to feature extraction in order to enhance the extraction of channel-specific features. A compact convolutional network model uses for feature extraction in the middle part. Finally, the time characteristic information is obtained by using TCN. The results show that our network is a lightweight network that is characterized by few parameters and fast speed. Our network achieves an average accuracy of 80.71% on the BCI Competition IV-2a dataset.

Insights into brain perceptions of the different taste qualities and hedonic valence of food via scalp electroencephalogram

Investigating brain activity is essential for exploring taste-experience related cues. The paper aimed to explore implicit (unconscious) emotional or physiological responses related to taste experiences using scalp electroencephalogram (EEG). We performed implicit measures of tastants of differing perceptual types (bitter, salty, sour and sweet) and intensities (low, medium, and high). The results showed that subjects were partially sensitive to different sensory intensities, i.e., for high intensities, taste stimuli could induce activation of different rhythm signals in the brain, with α and θ bands possibly being more sensitive to different taste types. Furthermore, the neural representations and corresponding sensory qualities (e.g., "sweet: pleasant" or "bitter: unpleasant") of different tastes could be discriminated at 250-1,500 ms after stimulus onset, and different tastes exhibited distinct temporal dynamic differences. Source localization indicated that different taste types activate brain areas associated with emotional eating, reward processing, and motivated tendencies, etc. Overall, our findings reveal a larger sophisticated taste map that accounted for the diversity of taste types in the human brain and assesses the emotion, reward, and motivated behavior represented by different tastes. This study provided basic insights and a perceptual foundation for the relationship between taste experience-related decisions and the prediction of brain activity.

Vibrotactile enhancement in hand rehabilitation has a reinforcing effect on sensorimotor brain activities

Objective

Stroke patients often suffer from hand dysfunction or loss of tactile perception, which in turn interferes with hand rehabilitation. Tactile-enhanced multi-sensory feedback rehabilitation is an approach worth considering, but its effectiveness has not been well studied. By using functional near-infrared spectroscopy (fNIRS) to analyze the causal activity patterns in the sensorimotor cortex, the present study aims to investigate the cortical hemodynamic effects of hand rehabilitation training when tactile stimulation is applied, and to provide a basis for rehabilitation program development.

Methods

A vibrotactile enhanced pneumatically actuated hand rehabilitation device was tested on the less-preferred hand of 14 healthy right-handed subjects. The training tasks consisted of move hand and observe video (MO), move hand and vibration stimulation (MV), move hand, observe video, and vibration stimulation (MOV), and a contrast resting task. Region of interest (ROI), a laterality index (LI), and causal brain network analysis methods were used to explore the brain’s cortical blood flow response to a multi-sensory feedback rehabilitation task from multiple perspectives.

Results

(1) A more pronounced contralateral activation in the right-brain region occurred under the MOV stimulation. Rehabilitation tasks containing vibrotactile enhancement (MV and MOV) had significantly more oxyhemoglobin than the MO task at 5 s after the task starts, indicating faster contralateral activation in sensorimotor brain regions. (2) Five significant lateralized channel connections were generated under the MV and MOV tasks (p < 0.05), one significant lateralized channel connection was generated by the MO task, and the Rest were not, showing that MV and MOV caused stronger lateralization activation. (3) We investigated all thresholds of granger causality (GC) resulting in consistent relative numbers of effect connections. MV elicited stronger causal interactions between the left and right cerebral hemispheres, and at the GC threshold of 0.4, there were 13 causal network connection pairs for MV, 7 for MO, and 9 for MOV.

Conclusion

Vibrotactile cutaneous stimulation as a tactile enhancement can produce a stronger stimulation of the brain’s sensorimotor brain areas, promoting the establishment of neural pathways, and causing a richer effect between the left and right cerebral hemispheres. The combination of kinesthetic, vibrotactile, and visual stimulation can achieve a more prominent training efficiency from the perspective of functional cerebral hemodynamics.

Time-Varying Effective Connectivity for Describing the Dynamic Brain Networks of Post-stroke Rehabilitation

Hemiplegia is a common motor dysfunction caused by a stroke. However, the dynamic network mechanism of brain processing information in post-stroke hemiplegic patients has not been revealed when performing motor imagery (MI) tasks. We acquire electroencephalography (EEG) data from healthy subjects and post-stroke hemiplegic patients and use the Fugl-Meyer assessment (FMA) to assess the degree of motor function damage in stroke patients. Time-varying MI networks are constructed using the adaptive directed transfer function (ADTF) method to explore the dynamic network mechanism of MI in post-stroke hemiplegic patients. Finally, correlation analysis has been conducted to study potential relationships between global efficiency and FMA scores. The performance of our proposed method has shown that the brain network pattern of stroke patients does not significantly change from laterality to bilateral symmetry when performing MI recognition. The main change is that the contralateral motor areas of the brain damage and the effective connection between the frontal lobe and the non-motor areas are enhanced, to compensate for motor dysfunction in stroke patients. We also find that there is a correlation between FMA scores and global efficiency. These findings help us better understand the dynamic brain network of patients with post-stroke when processing MI information. The network properties may provide a reliable biomarker for the objective evaluation of the functional rehabilitation diagnosis of stroke patients.

Age-Related Changes in Functional Connectivity during the Sensorimotor Integration Detected by Artificial Neural Network

Large-scale functional connectivity is an important indicator of the brain’s normal functioning. The abnormalities in the connectivity pattern can be used as a diagnostic tool to detect various neurological disorders. The present paper describes the functional connectivity assessment based on artificial intelligence to reveal age-related changes in neural response in a simple motor execution task. Twenty subjects of two age groups performed repetitive motor tasks on command, while the whole-scalp EEG was recorded. We applied the model based on the feed-forward multilayer perceptron to detect functional relationships between five groups of sensors located over the frontal, parietal, left, right, and middle motor cortex. Functional dependence was evaluated with the predicted and original time series coefficient of determination. Then, we applied statistical analysis to highlight the significant features of the functional connectivity network assessed by our model. Our findings revealed the connectivity pattern is consistent with modern ideas of the healthy aging effect on neural activation. Elderly adults demonstrate a pronounced activation of the whole-brain theta-band network and decreased activation of frontal–parietal and motor areas of the mu-band. Between-subject analysis revealed a strengthening of inter-areal task-relevant links in elderly adults. These findings can be interpreted as an increased cognitive demand in elderly adults to perform simple motor tasks with the dominant hand, induced by age-related working memory decline.