Using Brain-Computer Interface to Control a Virtual Drone Using Non-Invasive Motor Imagery and Machine Learning

In recent years, the control of devices “by the power of the mind” has become a very controversial topic but has also been very well researched in the field of state-of-the-art gadgets, such as smartphones, laptops, tablets and even smart TVs, and also in medicine, to be used by people with disabilities for whom these technologies may be the only way to communicate with the outside world. It is well known that BCI control is a skill and can be improved through practice and training. This paper aims to improve and diversify signal processing methods for the implementation of a brain-computer interface (BCI) based on neurological phenomena recorded during motor tasks using motor imagery (MI). The aim of the research is to extract, select and classify the characteristics of electroencephalogram (EEG) signals, which are based on sensorimotor rhythms, for the implementation of BCI systems. This article investigates systems based on brain-computer interfaces, especially those that use the electroencephalogram as a method of acquisition of MI tasks. The purpose of this article is to allow users to manipulate quadcopter virtual structures (external, robotic objects) simply through brain activity, correlated with certain mental tasks using undecimal transformation (UWT) to reduce noise, Independent Component Analysis (ICA) together with determination coefficient (r2) and, for classification, a hybrid neural network consisting of Radial Basis Functions (RBF) and a multilayer perceptron–recurrent network (MLP–RNN), obtaining a classification accuracy of 95.5%. Following the tests performed, it can be stated that the use of biopotentials in human–computer interfaces is a viable method for applications in the field of BCI. The results presented show that BCI training can produce a rapid change in behavioral performance and cognitive properties. If more than one training session is used, the results may be beneficial for increasing poor cognitive performance. To achieve this goal, three steps were taken: understanding the functioning of BCI systems and the neurological phenomena involved; acquiring EEG signals based on sensorimotor rhythms recorded during MI tasks; applying and optimizing extraction methods, selecting and classifying characteristics using neuronal networks.

Gymnasts' Ability to Modulate Sensorimotor Rhythms During Kinesthetic Motor Imagery of Sports Non-specific Movements Superior to Non-gymnasts

Previous psychological studies using questionnaires have consistently reported that athletes have superior motor imagery ability, both for sports-specific and for sports-non-specific movements. However, regarding motor imagery of sports-non-specific movements, no physiological studies have demonstrated differences in neural activity between athletes and non-athletes. The purpose of this study was to examine the differences in sensorimotor rhythms during kinesthetic motor imagery (KMI) of sports-non-specific movements between gymnasts and non-gymnasts. We selected gymnasts as an example population because they are likely to have particularly superior motor imagery ability due to frequent usage of motor imagery, including KMI as part of daily practice. Healthy young participants (16 gymnasts and 16 non-gymnasts) performed repeated motor execution and KMI of sports-non-specific movements (wrist dorsiflexion and shoulder abduction of the dominant hand). Scalp electroencephalogram (EEG) was recorded over the contralateral sensorimotor cortex. During motor execution and KMI, sensorimotor EEG power is known to decrease in the α- (8–15 Hz) and β-bands (16–35 Hz), referred to as event-related desynchronization (ERD). We calculated the maximal peak of ERD both in the α- (αERDmax) and β-bands (βERDmax) as a measure of changes in corticospinal excitability. αERDmax was significantly greater in gymnasts, who subjectively evaluated their KMI as being more vivid in the psychological questionnaire. On the other hand, βERDmax was greater in gymnasts only for shoulder abduction KMI. These findings suggest gymnasts' signature of flexibly modulating sensorimotor rhythms with no movements, which may be the basis of their superior ability of KMI for sports-non-specific movements.

Brain Symmetry Analysis during the Use of a BCI Based on Motor Imagery for the Control of a Lower-Limb Exoskeleton

Brain–Computer Interfaces (BCI) are systems that allow external devices to be controlled by means of brain activity. There are different such technologies, and electroencephalography (EEG) is an example. One of the most common EEG control methods is based on detecting changes in sensorimotor rhythms (SMRs) during motor imagery (MI). The aim of this study was to assess the laterality of cortical function when performing MI of the lower limb. Brain signals from five subjects were analyzed in two conditions, during exoskeleton-assisted gait and while static. Three different EEG electrode configurations were evaluated: covering both hemispheres, covering the non-dominant hemisphere and covering the dominant hemisphere. In addition, the evolution of performance and laterality with practice was assessed. Although sightly superior results were achieved with information from all electrodes, differences between electrode configurations were not statistically significant. Regarding the evolution during the experimental sessions, the performance of the BCI generally evolved positively the higher the experience was.

Does Real-Time Feedback Affect Sensorimotor EEG Patterns in Routine Motor Imagery Practice?

Background. Motor imagery engages much of the same neural circuits as an overt movement. Therefore, the mental rehearsal of movements is often used to supplement physical training and might aid motor neurorehabilitation after stroke. One attempt to capture the brain’s involvement in imagery involves the use, as a marker, of the depression or event-related desynchronization (ERD) of thalamocortical sensorimotor rhythms found in a human electroencephalogram (EEG). Using fast real-time processing, it is possible to make the subject aware of their own brain reactions or—even better—to turn them into actions through a technology called the brain–computer interface (BCI). However, it remains unclear whether BCI-enabled imagery facilitates a stronger or qualitatively different brain response compared to the open-loop training. Methods. Seven healthy volunteers who were experienced in both closed and open-loop motor imagery took part in six experimental sessions over a period of 4.5 months, in which they performed kinesthetic imagery of a previously known set of finger and arm movements with simultaneous 30-channel EEG acquisition. The first and the last session mostly consisted of feedback trials in which the subjects were presented with the classification results of the EEG patterns in real time; during the other sessions, no feedback was provided. Spatiotemporal and amplitude features of the ERD patterns concomitant with imagery were compared across experimental days and between feedback conditions using linear mixed-effects modeling. Results. The main spatial sources of ERD appeared to be highly stable across the six experimental days, remaining nearly identical in five of seven subjects (Pearson’s ρ > 0.94). Only in one subject did the spatial pattern of activation statistically significantly differ (p = 0.009) between the feedback and no-feedback conditions. Real-time visual feedback delivered through the BCI did not significantly increase the ERD strength. Conclusion. The results imply that the potential benefits of MI could be yielded by well-habituated subjects with a simplified open-loop setup, e.g., through at-home self-practice.

Handedness effects on motor imagery during kinesthetic and visual-motor conditions

Recent studies show that during a simple movement imagery task, the power of sensorimotor rhythms differs according to handedness. However, the effects of motor imagery perspectives on these differences have not been investigated yet. Our study aimed to check how handedness impacts the activity of alpha (8–13 Hz) and beta (15–30 Hz) oscillations during creating a kinesthetic (KMI) or visual-motor (VMI) representation of movement. Forty subjects (20 right-handed and 20 left-handed) who participated in the experiment were tasked with imagining sequential finger movement from a visual or kinesthetic perspective. Both the electroencephalographic (EEG) activity and behavioral correctness of the imagery task performance were measured. After the registration, we used independent component analysis (ICA) on EEG data to localize visual- and motor-related EEG sources of activity shared by both motor imagery conditions. Significant differences were obtained in the visual cortex (the occipital ICs cluster) and the right motor-related area (right parietal ICs cluster). In comparison to right-handers who, regardless of the task, demonstrated the same pattern in the visual area, left-handers obtained higher power in the alpha waves in the VMI task and better performance in this condition. On the other hand, only the right-handed showed different patterns in the alpha waves in the right motor cortex during the KMI condition. The results indicate that left-handers imagine movement differently than right-handers, focusing on visual experience. This provides new empirical evidence on the influence of movement preferences on imagery processes and has possible future implications for research in the area of neurorehabilitation and motor imagery-based brain–computer interfaces (MI-BCIs).

Gymnasts' ability for general motor imagery evaluated by bioelectric sensorimotor rhythms

Introduction: Previous psychological studies using questionnaires have consistently reported that athletes have superior motor imagery ability, both for sports-specific movements and general movements. However, regarding general motor imagery, no physiological studies have demonstrated differences in neural activity between athletes and non-athletes. Gymnasts may be a suitable population for investigating this issue because they are likely to have particularly superior motor imagery ability due to their frequent usage of motor imagery as part of daily practice. Purpose: The purpose of the present study was to examine differences in bioelectric sensorimotor rhythms during kinesthetic motor imagery of general movements between gymnasts and non-gymnasts. Methods: Healthy young participants (16 gymnasts and 16 non-gymnasts) performed repeated motor execution and motor imagery of general movements (wrist dorsiflexion and shoulder abduction of the dominant hand). Scalp electroencephalogram (EEG) was recorded over the contralateral sensorimotor cortex. During motor execution and motor imagery, sensorimotor EEG power is known to decrease in the α- (8-15 Hz) and β-bands (16-35 Hz), referred to as event-related desynchronization (ERD). We calculated the maximal peak of ERD both in the α- (αERDmax) and β-bands (βERDmax) as a measure of changes in corticospinal excitability. Results: ERD magnitude during motor imagery was significantly greater in gymnasts, who subjectively evaluated their motor imagery as being more vivid. In particular, αERDmax was greater in gymnasts compared with non-gymnasts for both motor imagery tasks, whereas βERDmax was greater in gymnasts only for shoulder abduction imagery. Conclusion: We observed gymnasts' signature of flexibly modulating sensorimotor rhythm with no movement, which may be the basis of their superior general motor imagery ability.

User State Classification Based on Functional Brain Connectivity Using a Convolutional Neural Network

The brain–computer interface (BCI) is a promising technology where a user controls a robot or computer by thinking with no movement. There are several underlying principles to implement BCI, such as sensorimotor rhythms, P300, steady-state visually evoked potentials, and directional tuning. Generally, different principles are applied to BCI depending on the application, because strengths and weaknesses vary according to each BCI method. Therefore, BCI should be able to predict a user state to apply suitable principles to the system. This study measured electroencephalography signals in four states (resting, speech imagery, leg-motor imagery, and hand-motor imagery) from 10 healthy subjects. Mutual information from 64 channels was calculated as brain connectivity. We used a convolutional neural network to predict a user state, where brain connectivity was the network input. We applied five-fold cross-validation to evaluate the proposed method. Mean accuracy for user state classification was 88.25 ± 2.34%. This implies that the system can change the BCI principle using brain connectivity. Thus, a BCI user can control various applications according to their intentions.

Heart rate variability predicts decline in sensorimotor rhythm control

Voluntary control of sensorimotor rhythms (SMR, 8-12 Hz) can be used for brain-computer interface (BCI)-based operation of an assistive hand exoskeleton, e.g., in finger paralysis after stroke. To gain SMR control, stroke survivors are usually instructed to engage in motor imagery (MI) or to attempt moving the paralyzed fingers resulting in task- or event-related desynchronization (ERD) of SMR (SMR-ERD). However, as these tasks are cognitively demanding, especially for stroke survivors suffering from cognitive impairments, BCI control performance can deteriorate considerably over time. It would thus be important to identify biomarkers that predict decline in BCI control performance within an ongoing session in order to optimize the man-machine interaction scheme. Here we determine the link between BCI control performance over time and heart rate variability (HRV). Specifically, we investigated whether HRV can be used as a biomarker to predict decline during voluntary control of SMR-ERD across 17 healthy participants using Granger causality. SMR-ERD was visually displayed on a screen. Participants were instructed to engage in MI-based SMR-ERD control over two consecutive runs of 8.5 minutes each. During the second run, task difficulty was gradually increased. While control performance (p = .18) and HRV (p = .16) remained unchanged across participants during the first run, during the second run, both measures declined over time at high correlation (performance: -0.61%/10s, p = 0; HRV: -0.007ms/10s, p < .001). We found that HRV Granger-caused BCI control performance (p < .001) exhibited predictive characteristics of HRV on an individual participant level. These results suggest that HRV can predict decline in BCI performance paving the way for adaptive BCI control paradigms, e.g., to individualize and optimize assistive BCI systems in stroke.

Neurofeedback for Chronic Pain

Neurofeedback is a novel neuromodulatory therapy where individuals are given real-time feedback regarding their brain neurophysiological signals in order to increase volitional control over their brain activity. Such biofeedback platform can be used to increase an individual’s resilience to pain as chronic pain has been associated with abnormal central processing of ascending pain signals. Neurofeedback can be provided based on electroencephalogram (EEG) or functional magnetic resonance imaging (fMRI) recordings of an individual. Target brain rhythms commonly used in EEG neurofeedback for chronic pain include theta, alpha, beta and sensorimotor rhythms. Such training has not only been shown to improve pain in a variety of pain conditions such as central neuropathic pain, fibromyalgia, traumatic brain injury and chemotherapy induced peripheral neuropathy, but has also been shown to improve pain associated symptoms such as sleep, fatigue, depression and anxiety. Adverse events associated with neurofeedback training are often self-limited and resolve with decreased frequency of training. Provision of such training has also been explored in the home setting whereby individuals have been encouraged to practice this as and when required with promising results. Therefore, neurofeedback has the potential to provide low-cost yet holistic approach to the management of chronic pain.