Effects of Task Complexity on Motor Imagery-Based Brain-Computer Interface

Enhancing Brain-Computer Interface Performance by Incorporating Brain-to-Brain Coupling

Human cooperation relies on key features of social interaction in order to reach desirable outcomes. Similarly, human-robot interaction may benefit from integration with human-human interaction factors. In this paper, we aim to investigate brain-to-brain coupling during motor imagery (MI)-based brain-computer interface (BCI) training using eye-contact and hand-touch interaction. Twelve pairs of friends (experimental group) and 10 pairs of strangers (control group) were recruited for MI-based BCI tests concurrent with electroencephalography (EEG) hyperscanning. Event-related desynchronization (ERD) was estimated to measure cortical activation, and interbrain functional connectivity was assessed using multilevel statistical analysis. Furthermore, we compared BCI classification performance under different social interaction conditions. In the experimental group, greater ERD was found around the contralateral sensorimotor cortex under social interaction conditions compared with MI without any social interaction. Notably, EEG channels with decreased power were mainly distributed around the frontal, central, and occipital regions. A significant increase in interbrain coupling was also found under social interaction conditions. BCI decoding accuracies were significantly improved in the eye contact condition and eye and hand contact condition compared with the no-interaction condition. However, for the strangers' group, no positive effects were observed in comparisons of cortical activations between interaction and no-interaction conditions. These findings indicate that social interaction can improve the neural synchronization between familiar partners with enhanced brain activations and brain-to-brain coupling. This study may provide a novel method for enhancing MI-based BCI performance in conjunction with neural synchronization between users.

Subjective time perception in musical imagery: An fMRI study on musicians

The cognitive preparation of an operation without overt motor execution is referred to as imagery (of any kind). Over the last two decades of progress in brain timing studies, the timing of imagery has received little focus. This study compared the time perception of ten professional violinists' actual and imagery performances to see if such an analysis could offer a different model of timing in musicians' imagery skills. When comparing the timing profiles of the musicians between the two situations (actual and imagery), we found a significant correlation in overestimation of time in the imagery. In our fMRI analysis, we found high activation in the left cerebellum. This finding seems consistent with dedicated models of timing such as the cerebellar timing hypothesis, which assigns a "specialized clock" for tasks. In addition, the present findings might provide empirical data concerning imagery, creativity, and time. Maintaining imagery over time is one of the foundations of creativity, and understanding the underlying temporal neuronal mechanism might help us to apprehend the machinery of creativity per se.

Electroencephalogram based brain-computer interface: Applications, challenges, and opportunities

Brain-Computer Interfaces (BCI) is an exciting and emerging research area for researchers and scientists. It is a suitable combination of software and hardware to operate any device mentally. This review emphasizes the significant stages in the BCI domain, current problems, and state-of-the-art findings. This article also covers how current results can contribute to new knowledge about BCI, an overview of BCI from its early developments to recent advancements, BCI applications, challenges, and future directions. The authors pointed to unresolved issues and expressed how BCI is valuable for analyzing the human brain. Humans' dependence on machines has led humankind into a new future where BCI can play an essential role in improving this modern world.

Decoding EEG rhythms offline and online during motor imagery for standing and sitting based on a brain-computer interface

Motor imagery (MI)-based brain-computer interface (BCI) systems have shown promising advances for lower limb motor rehabilitation. The purpose of this study was to develop an MI-based BCI for the actions of standing and sitting. Thirty-two healthy subjects participated in the study using 17 active EEG electrodes. We used a combination of the filter bank common spatial pattern (FBCSP) method and the regularized linear discriminant analysis (RLDA) technique for decoding EEG rhythms offline and online during motor imagery for standing and sitting. The offline analysis indicated the classification of motor imagery and idle state provided a mean accuracy of 88.51 ± 1.43% and 85.29 ± 1.83% for the sit-to-stand and stand-to-sit transitions, respectively. The mean accuracies of the sit-to-stand and stand-to-sit online experiments were 94.69 ± 1.29% and 96.56 ± 0.83%, respectively. From these results, we believe that the MI-based BCI may be useful to future brain-controlled standing systems.

Discriminative Frequencies and Temporal EEG Segmentation in the Motor Imagery Classification Approach

A linear discriminant analysis transformation-based approach to the classification of three different motor imagery types for brain–computer interfaces was considered. The study involved 16 conditionally healthy subjects (12 men, 4 women, mean age of 21.5 years). First, the search for subject-specific discriminative frequencies was conducted in the task of movement-related activity. This procedure was shown to increase the classification accuracy compared to the conditional common spatial pattern (CSP) algorithm, followed by a linear classifier considered as a baseline approach. In addition, an original approach to finding discriminative temporal segments for each motor imagery was tested. This led to a further increase in accuracy under the conditions of using Hjorth parameters and interchannel correlation coefficients as features calculated for the EEG segments. In particular, classification by the latter feature led to the best accuracy of 71.6%, averaged over all subjects (intrasubject classification), and, surprisingly, it also allowed us to obtain a comparable value of intersubject classification accuracy of 68%. Furthermore, scatter plots demonstrated that two out of three pairs of motor imagery were discriminated by the approach presented.

Effects of task complexity or rate of motor imagery on motor learning in healthy young adults

BACKGROUND

A growing body of evidence suggests the benefit of motor imagery in motor learning. While some studies tried to look at the effect of isolated mental practice, others evaluated the combined effect of motor imagery and physical practice in clinical rehabilitation. This study aimed to investigate the effects of task complexity or rates of motor imagery on motor learning in health young adults.

METHODS

Eighty-eight healthy individuals participated in this study. Participants were randomly allocated to either Group A (50% complex, N = 22), Group B (75% complex, N = 22), Group C (50% simple, N = 22), or Group D (75% simple, N = 22). Participants in the complex groups performed their task with nondominant hand and those in simple groups with a dominant hand. All participants performed a task that involved reach, grasp, and release tasks. The performance of the four groups was examined in the acquisition and retention phase. The main outcome measure was the movement time.

RESULTS

There were significant differences between immediate (i.e., acquisition) and late (i.e., retention) movement times at all three stages of task (i.e., MT1 [reaching time], MT2 [target transport time], and TMT [reaching time plus object transport time]) when individuals performed complex task with 75% imagery rate (p < .05). Similarly, there were significant differences between immediate and late movement times at all stages of task except the MT2 when individuals performed simple task with 75% imagery rate (p < .05). There were significant effects of task complexity (simple vs. complex tasks) on immediate movement time at the first stage of task (i.e., MT1 ) and late movement times of all three stages of task (p < .05). There were significant effects of the rate of imagery (50% vs. 75%) on late movement times at all three stages of tasks (p > .05). Additionally, there were no interaction effects of either task complexity or rate of imagery on both immediate and late movement times at all three stages of tasks (p > .05).

CONCLUSION

This study supports the use of higher rates (75%) of motor imagery to improve motor learning. Additionally, the practice of a complex task demonstrated better motor learning in healthy young adults. Future longitudinal studies should validate these results in different patient's population such as stroke, spinal cord injury, and Parkinson's disease.

Closed-Loop Phase-Dependent Vibration Stimulation Improves Motor Imagery-Based Brain-Computer Interface Performance

The motor imagery (MI) paradigm has been wildly used in brain-computer interface (BCI), but the difficulties in performing imagery tasks limit its application. Mechanical vibration stimulus has been increasingly used to enhance the MI performance, but its improvement consistence is still under debate. To develop more effective vibration stimulus methods for consistently enhancing MI, this study proposes an EEG phase-dependent closed-loop mechanical vibration stimulation method. The subject's index finger of the non-dominant hand was given 4 different vibration stimulation conditions (i.e., continuous open-loop vibration stimulus, two different phase-dependent closed-loop vibration stimuli and no stimulus) when performing two tasks of imagining movement and rest of the index finger from his/her dominant hand. We compared MI performance and brain oscillatory patterns under different conditions to verify the effectiveness of this method. The subjects performed 80 trials of each type in a random order, and the average phase-lock value of closed-loop stimulus conditions was 0.71. It was found that the closed-loop vibration stimulus applied in the falling phase helped the subjects to produce stronger event-related desynchronization (ERD) and sustain longer. Moreover, the classification accuracy was improved by about 9% compared with MI without any vibration stimulation (p = 0.012, paired t-test). This method helps to modulate the mu rhythm and make subjects more concentrated on the imagery and without negative enhancement during rest tasks, ultimately improves MI-based BCI performance. Participants reported that the tactile fatigue under closed-loop stimulation conditions was significantly less than continuous stimulation. This novel method is an improvement to the traditional vibration stimulation enhancement research and helps to make stimulation more precise and efficient.

A Maximum Likelihood Perspective of Spatial Filter Design in SSVEP-Based BCIs

In steady-state visual-evoked potential (SSVEP) based brain-computer interfaces (BCIs), existing detection algorithms utilizing spatial filters like task-related component analysis (TRCA) derive the spatial filters mainly through maximizing the inter-trial similarity between the combined signals over the training set. Although they achieve by far the best classification performance in SSVEP-based BCIs, some important problems are still unresolved. Especially, the mechanism of how spatial filters cancel the background noise in brain signals and optimize the signal-to-noise ratio (SNR) of SSVEPs is still not figured out. Therefore, to solve these problems, in this paper a new perspective of spatial filter design is proposed. Specifically, a linear generative signal model of SSVEP is adopted and the spatial filters are obtained automatically through maximum likelihood estimation of source signals and channel vectors. In the same time, the relation between maximum likelihood estimation and signal-to-noise ratio (SNR) maximization is discussed. Through a step-by-step formulation, this paper provides a theoretical justification for those conventional algorithms utilizing spatial filters. As for the classification performance, the proposed scheme is tested on a benchmark dataset of 35 subjects. Experiment results show that the classification performance of the proposed scheme is competitive against three benchmark algorithms, which include TRCA. Especially, the proposed scheme achieves a fair performance improvement over the benchmark methods in the cases where a shorter time window, or a larger number of electrodes, or a smaller number of training blocks are adopted.

Deep stacked support matrix machine based representation learning for motor imagery EEG classification

BACKGROUND AND OBJECTIVE

Electroencephalograph (EEG) classification is an important technology that can establish a mapping relationship between EEG features and cognitive tasks. Emerging matrix classifiers have been successfully applied to motor imagery (MI) EEG classification, but they belong to shallow classifiers, making powerful stacked generalization principle not exploited for automatically learning deep EEG features. To learn the high-level representation and abstraction, we proposed a novel deep stacked support matrix machine (DSSMM) to improve the performance of existing shallow matrix classifiers in EEG classification.

METHODS

The main idea of our framework is founded on the stacked generalization principle, where support matrix machine (SMM) is introduced as the basic building block of deep stacked network. The weak predictions of all previous layers obtained via SMM are randomly projected to help move apart the manifold of the original input EEG feature, and then the newly generated features are fed into the next layer of DSSMM. The framework only involves an efficient feed-forward rather than parameter fine-tuning with backpropagation, each layer of which is a convex optimization problem, thus simplifying the objective function solving process.

RESULTS

Extensive experiments on three public EEG datasets and a self-collected EEG dataset are conducted. Experimental results demonstrate that our DSSMM outperforms the available state-of-the-art methods.

CONCLUSION

The proposed DSSMM inherits the characteristic of matrix classifiers that can learn the structural information of data as well as the powerful capability of deep representation learning, which makes it adapted to classify complex matrix-form EEG data.