Performance Variation of a Somatosensory BCI Based on Imagined Sensation: A Large Population Study

Tactile versus motor imagery: differences in corticospinal excitability assessed with single-pulse TMS

Tactile Imagery (TI) remains a fairly understudied phenomenon despite growing attention to this topic in recent years. Here, we investigated the effects of TI on corticospinal excitability by measuring motor evoked potentials (MEPs) induced by single-pulse transcranial magnetic stimulation (TMS). The effects of TI were compared with those of tactile stimulation (TS) and kinesthetic motor imagery (kMI). Twenty-two participants performed three tasks in randomly assigned order: imagine finger tapping (kMI); experience vibratory sensations in the middle finger (TS); and mentally reproduce the sensation of vibration (TI). MEPs increased during both kMI and TI, with a stronger increase for kMI. No statistically significant change in MEP was observed during TS. The demonstrated differential effects of kMI, TI and TS on corticospinal excitability have practical implications for devising the imagery-based and TS-based brain-computer interfaces (BCIs), particularly the ones intended to improve neurorehabilitation by evoking plasticity changes in sensorimotor circuitry.

Neural Correlates of Motor/Tactile Imagery and Tactile Sensation in a BCI paradigm: A High-Density EEG Source Imaging Study

Complementary to brain-computer interface (BCI) based on motor imagery (MI) task, sensory imagery (SI) task provides a way for BCI construction using brain activity from somatosensory cortex. The underlying neurophysiological correlation between SI and MI was unclear and difficult to measure through behavior recording. In this study, we investigated the underlying neurodynamic of motor/tactile imagery and tactile sensation tasks through a high-density electroencephalogram (EEG) recording, and EEG source imaging was used to systematically explore the cortical activation differences and correlations between the tasks. In the experiment, participants were instructed to perform the left and right hand tasks in MI paradigm, sensory stimulation (SS) paradigm and SI paradigm. The statistical results demonstrated that the imagined MI and SI tasks differed from each other within ipsilateral sensorimotor scouts, frontal and right temporal areas in α bands, whereas real SS and imagined SI showed a similar activation pattern. The similarity between SS and SI may provide a way to train the BCI system, while the difference between MI and SI may provide a way to integrate the discriminative information between them to enhance BCI performance. The combination of the tasks and its underlying neurodynamic would provide a new approach for BCI designation for a wider application. BCI studies concentrate on the hybrid decoding method combining MI or SI with SS, but the underlining neurophysiological correlates between them were unclear. MI and SI differed from each other within the ipsilateral sensorimotor cortex in alpha bands. This is a first study to investigate the neurophysiological relationship between MI and SI through an EEG source imaging approach from high-density EEG recording.

Cortical activation and BCI performance during brief tactile imagery: A comparative study with motor imagery

Brain-computer interfaces (BCIs) rely heavily on motor imagery (MI) for operation, yet tactile imagery (TI) presents a novel approach that may be advantageous in situations where visual feedback is impractical. The current study aimed to compare the cortical activity and digit classification performance induced by TI and MI to assess the viability of TI for use in BCIs. Twelve right-handed participants engaged in trials of TI and MI, focusing on their left and right index digits. Event-related desynchronization (ERD) in the mu and beta bands was analyzed, and classification accuracy was determined through an artificial neural network (ANN). Comparable ERD patterns were observed in both TI and MI, with significant decreases in ERD during imagery tasks. The ANN demonstrated high classification accuracy, with TI achieving a mean±SD of 79.30 ± 3.91 % and MI achieving 81.10 ± 2.96 %, with no significant difference between the two (p = 0.11). The study found that TI induces substantial ERD comparable to MI and maintains high classification accuracy, supporting its potential as an effective mental strategy for BCIs. This suggests that TI could be a valuable alternative in BCI applications, particularly for individuals unable to rely on visual cues.

Detection of tactile-based error-related potentials (ErrPs) in human-robot interaction

Robot learning based on implicitly extracted error detections (e.g., EEG-based error detections) has been well-investigated in human-robot interaction (HRI). In particular, the use of error-related potential (ErrP) evoked when recognizing errors is advantageous for robot learning when evaluation criteria cannot be explicitly defined, e.g., due to the complex behavior of robots. In most studies, erroneous behavior of robots were recognized visually. In some studies, visuo-tactile stimuli were used to evoke ErrPs or a tactile cue was used to indicate upcoming errors. To our knowledge, there are no studies in which ErrPs are evoked when recognizing errors only via the tactile channel. Hence, we investigated ErrPs evoked by tactile recognition of errors during HRI. In our scenario, subjects recognized errors caused by incorrect behavior of an orthosis during the execution of arm movements tactilely. EEG data from eight subjects was recorded. Subjects were asked to give a motor response to ensure error detection. Latency between the occurrence of errors and the response to errors was expected to be short. We assumed that the motor related brain activity is timely correlated with the ErrP and might be used from the classifier. To better interpret and test our results, we therefore tested ErrP detections in two additional scenarios, i.e., without motor response and with delayed motor response. In addition, we transferred three scenarios (motor response, no motor response, delayed motor response). Response times to error was short. However, high ErrP-classification performance was found for all subjects in case of motor response and no motor response condition. Further, ErrP classification performance was reduced for the transfer between motor response and delayed motor response, but not for the transfer between motor response and no motor response. We have shown that tactilely induced errors can be detected with high accuracy from brain activity. Our preliminary results suggest that also in tactile ErrPs the brain response is clear enough such that motor response is not relevant for classification. However, in future work, we will more systematically investigate tactile-based ErrP classification.

Enhanced Motor Imagery Decoding by Calibration Model-Assisted With Tactile ERD

OBJECTIVE

In this study, we propose a tactile-assisted calibration method for a motor imagery (MI) based Brain-Computer Interface (BCI) system.

METHOD

In the proposed calibration, tactile stimulation was applied to the hand wrist to assist the subjects in the MI task, which is named SA-MI task. Then, classifier training in the SA-MI Calibration was performed using the SA-MI data, while the Conventional Calibration employed the MI data. After the classifiers were trained, the performance was evaluated on a common MI dataset.

RESULTS

Our study demonstrated that the SA-MI Calibration significantly improved the performance as compared with the Conventional Calibration, with a decoding accuracy of (78.3% vs. 71.3%). Moreover, the average calibration time could be reduced by 40%. This benefit of the SA-MI Calibration effect was further validated by an independent control group, which showed no improvement when tactile stimulation was not applied during the calibration phase. Further analysis showed that when compared with MI, greater motor-related cortical activation and higher R 2 value in the alpha-beta frequency band were induced in SA-MI.

CONCLUSION

Indeed, the SA-MI Calibration could significantly improve the performance and reduce the calibration time as compared with the Conventional Calibration.

SIGNIFICANCE

The proposed tactile stimulation-assisted MI Calibration method holds great potential for a faster and more accurate system setup at the beginning of BCI usage.

Evaluation of EEG Oscillatory Patterns and Classification of Compound Limb Tactile Imagery

Objective: The purpose of this study was to investigate the cortical activity and digit classification performance during tactile imagery (TI) of a vibratory stimulus at the index, middle, and thumb digits within the left hand in healthy individuals. Furthermore, the cortical activities and classification performance of the compound TI were compared with similar compound motor imagery (MI) with the same digits as TI in the same subjects. Methods: Twelve healthy right-handed adults with no history of upper limb injury, musculoskeletal condition, or neurological disorder participated in the study. The study evaluated the event-related desynchronization (ERD) response and brain-computer interface (BCI) classification performance on discriminating between the digits in the left-hand during the imagery of vibrotactile stimuli to either the index, middle, or thumb finger pads for TI and while performing a motor activity with the same digits for MI. A supervised machine learning technique was applied to discriminate between the digits within the same given limb for both imagery conditions. Results: Both TI and MI exhibited similar patterns of ERD in the alpha and beta bands at the index, middle, and thumb digits within the left hand. While TI had significantly lower ERD for all three digits in both bands, the classification performance of TI-based BCI (77.74 ± 6.98%) was found to be similar to the MI-based BCI (78.36 ± 5.38%). Conclusions: The results of this study suggest that compound tactile imagery can be a viable alternative to MI for BCI classification. The study contributes to the growing body of evidence supporting the use of TI in BCI applications, and future research can build on this work to explore the potential of TI-based BCI for motor rehabilitation and the control of external devices.

The Effect of Tactile Imagery Training on Reaction Time in Healthy Participants

BACKGROUND

Reaction time is an important measure of sensorimotor performance and coordination and has been shown to improve with training. Various training methods have been employed in the past to improve reaction time. Tactile imagery (TI) is a method of mentally simulating a tactile sensation and has been used in brain-computer interface applications. However, it is yet unknown whether TI can have a learning effect and improve reaction time.

OBJECTIVE

The purpose of this study was to investigate the effect of TI on reaction time in healthy participants.

METHODS

We examined the reaction time to vibratory stimuli before and after a TI training session in an experimental group and compared the change in reaction time post-training with pre-training in the experimental group as well as the reaction time in a control group. A follow-up evaluation of reaction time was also conducted.

RESULTS

The results showed that TI training significantly improved reaction time after TI compared with before TI by approximately 25% (pre-TI right-hand mean ± SD: 456.62 ± 124.26 ms, pre-TI left-hand mean ± SD: 448.82 ± 124.50 ms, post-TI right-hand mean ± SD: 340.32 ± 65.59 ms, post-TI left-hand mean ± SD: 335.52 ± 59.01 ms). Furthermore, post-training reaction time showed significant reduction compared with the control group and the improved reaction time had a lasting effect even after four weeks post-training.

CONCLUSION

These findings indicate that TI training may serve as an alternate imagery strategy for improving reaction time without the need for physical practice.

Analysis of instantaneous brain interactions contribution to a motor imagery classification task

The purpose of this study is to analyze the contribution of the interactions between electrodes, measured either as correlation or as Jaccard distance, to the classification of two actions in a motor imagery paradigm, namely, left-hand movement and right-hand movement. The analysis is performed in two classifier models, namely, a static (linear discriminant analysis, LDA) model and a dynamic (hidden conditional random field, HCRF) model. The impact of using the sliding window technique (SWT) in the static and dynamic models is also analyzed. The study proved that their combination with temporal features provides significant information to improve the classification in a two-class motor imagery task for LDA (average accuracy: 0.7192 no additional features, 0.7617 by adding correlation, 0.7606 by adding Jaccard distance; p < 0.001) and HCRF (average accuracy: 0.7370 no additional features, 0.7764 by adding correlation, 0.7793 by adding Jaccard distance; p < 0.001). Also, we showed that adding interactions between electrodes improves significantly the performance of each classifier, regarding the nature of the interaction measure or the classifier itself.