EEG signatures of low back and knee joint pain during movement execution: a short report.

Chronic musculoskeletal pain has a high prevalence between European citizens, affecting their quality of life and their ability to work. The plastic changes associated with the occurrence of chronic musculoskeletal pain are still not fully understood. The current short report investigated the possible changes in brain activity caused by pain during movement in two of the most common musculoskeletal pain disorders in Denmark, knee pain and low back pain. Electroencephalography (EEG) was recorded from 20 participants (5 participants with knee pain, 5 with low back pain and 10 healthy controls). Participants with pain performed a movement that evoked pain in the area of interest, and the healthy controls performed the same movement. Electromyographic (EMG) signals were also collected to identify movement initiation. No differences were observed in brain activity of participants with pain and healthy controls during rest. During movement execution, though, participants with pain showed significantly higher event related synchronization in the alpha and beta bands compared to healthy controls. These changes could be related to higher cognitive processing, possibly due to the attempt of suppressing the pain. These results highlight the importance of assessing cortical activity during movement to reveal plastic changes due to musculoskeletal pain. This adds to our knowledge regarding plastic changes in cortical activity related to musculoskeletal pain in different locations. Such knowledge could help us identify neurophysiological markers for clinical changes and contribute to the development of new treatment approaches based on neuromodulation such as neurofeedback.

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

A proportion of users cannot achieve adequate brain-computer interface (BCI) control. The diversity of BCI modalities provides a way to solve this emerging issue. Here, we investigate the accuracy of a somatosensory BCI based on sensory imagery (SI). During the SI tasks, subjects were instructed to imagine a tactile sensation and to maintain the attention on the corresponding hand, as if there was tactile stimulus on the skin of the wrist. The performance across 106 healthy subjects in left- and right-hand SI discrimination was 78.9±13.2%. In 70.7% of the subjects the performance was above 70%. The SI task induced a contralateral cortical activation, and high-density EEG source localization showed that the real tactile stimulation and imagined tactile stimulation shared similar cortical activations within the somatosensory cortex. The somatosensory BCI based on SI provides a new signal modality for independent BCI development. Moreover, a combination of SI and other BCI modalities, such as motor imagery, may provide new avenues for further improving BCI usage and applicability, especially in those subjects unable to attain adequate BCI control with conventional BCI modalities.

Reducing the Calibration Time in Somatosensory BCI by Using Tactile ERD.

OBJECTIVE: We propose a tactile-induced-oscillation approach to reduce the calibration time in somatosensory brain-computer interfaces (BCI). METHODS: Based on the similarity between tactile induced event-related desynchronization (ERD) and imagined sensation induced ERD activation, we extensively evaluated BCI performance when using a conventional and a novel calibration strategy. In the conventional calibration, the tactile imagined data was used, while in the sensory calibration model sensory stimulation data was used. Subjects were required to sense the tactile stimulus when real tactile was applied to the left or right wrist and were required to perform imagined sensation tasks in the somatosensory BCI paradigm. RESULTS: The sensory calibration led to a significantly better performance than the conventional calibration when tested on the same imagined sensation dataset ( [Formula: see text]=10.89, P=0.0038), with an average 5.1% improvement in accuracy. Moreover, the sensory calibration was 39.3% faster in reaching a performance level of above 70% accuracy. CONCLUSION: The proposed approach of using tactile ERD from the sensory cortex provides an effective way of reducing the calibration time in a somatosensory BCI system. SIGNIFICANCE: The tactile stimulation would be specifically useful before BCI usage, avoiding excessive fatigue when the mental task is difficult to perform. The tactile ERD approach may find BCI applications for patients or users with preserved afferent pathways.

Short-interval intracortical inhibition and facilitation targeting upper and lower limb muscles.

Transcranial magnetic stimulation (TMS) can be used to study excitability of corticospinal neurons in human motor cortex. It is currently not fully elucidated if corticospinal neurons in the hand vs. leg representation show the same or different regulation of their excitability by GABAAergic and glutamatergic interneuronal circuitry. Using a paired-pulse TMS protocol we tested short-interval intracortical inhibition (SICI) and short-interval intracortical facilitation (SICF) in 18 healthy participants. Motor evoked potentials were evoked in one hand (abductor digiti minimi) and one leg muscle (tibialis anterior), with systematic variation of the intensities of the first (S1) and second (S2) pulse between 60 and 140% resting motor threshold (RMT) in 10% steps, at two interstimulus intervals of 1.5 and 2.1 ms. For the hand and leg motor representations and for both interstimulus intervals, SICI occurred if the intensities of S1 < RMT and S2 > RMT, while SICF predominated if S1 = S2 ≤ RMT, or S1 > RMT and S2 < RMT. Findings confirm and extend previous evidence that the regulation of excitability of corticospinal neurons of the hand versus leg representation in human primary cortex through GABAAergic and glutamatergic interneuronal circuits is highly similar, and that corticospinal neurons of both representations are activated by TMS transsynaptically in largely identical ways.

Participant-specific classifier tuning increases the performance of hand movement detection from EEG in patients with amyotrophic lateral sclerosis.

Objective.Brain-computer interface (BCI) systems can be employed to provide motor and communication assistance to patients suffering from neuromuscular diseases, such as amyotrophic lateral sclerosis (ALS). Movement related cortical potentials (MRCPs), which are naturally generated during movement execution, can be used to implement a BCI triggered by motor attempts. Such BCI could assist impaired motor functions of ALS patients during disease progression, and facilitate the training for the generation of reliable MRCPs. The training aspect is relevant to establish a communication channel in the late stage of the disease. Therefore, the aim of this study was to investigate the possibility of detecting MRCPs associated to movement intention in ALS patients with different levels of disease progression from slight to complete paralysis.Approach.Electroencephalography signals were recorded from nine channels in 30 ALS patients at various stages of the disease while they performed or attempted to perform hand movements timed to a visual cue. The movement detection was implemented using offline classification between movement and rest phase. Temporal and spectral features were extracted using 500 ms sliding windows with 50% overlap. The detection was tested for each individual channel and two surrogate channels by performing feature selection followed by classification using linear and non-linear support vector machine and linear discriminant analysis.Main results.The results demonstrated that the detection performance was high in all patients (accuracy 80.5 ± 5.6%) but that the classification parameters (channel, features and classifier) leading to the best performance varied greatly across patients. When the same channel and classifier were used for all patients (participant-generic analysis), the performance significantly decreased (accuracy 74 ± 8.3%).Significance.The present study demonstrates that to maximize the detection of brain waves across ALS patients at different stages of the disease, the classification pipeline should be tuned to each patient individually.

Online control of an assistive active glove by slow cortical signals in patients with amyotrophic lateral sclerosis.

Objective.A brain-computer interface (BCI) allows users to control external devices using brain signals that can be recorded non-invasively via electroencephalography (EEG). Movement related cortical potentials (MRCPs) are an attractive option for BCI control since they arise naturally during movement execution and imagination, and therefore, do not require an extensive training. This study tested the feasibility of online detection of reaching and grasping using MRCPs for the application in patients suffering from amyotrophic lateral sclerosis (ALS).Approach.A BCI system was developed to trigger closing of a soft assistive glove by detecting a reaching movement. The custom-made software application included data collection, a novel method for collecting the input data for classifier training from the offline recordings based on a sliding window approach, and online control of the glove. Eight healthy subjects and two ALS patients were recruited to test the developed BCI system. They performed assessment blocks without the glove active (NG), in which the movement detection was indicated by a sound feedback, and blocks (G) in which the glove was controlled by the BCI system. The true positive rate (TPR) and the positive predictive value (PPV) were adopted as the outcome measures. Correlation analysis between forehead EEG detecting ocular artifacts and sensorimotor area EEG was conducted to confirm the validity of the results.Main results.The overall median TPR and PPV were >0.75 for online BCI control, in both healthy individuals and patients, with no significant difference across the blocks (NG versus G).Significance.The results demonstrate that cortical activity during reaching can be detected and used to control an external system with a limited amount of training data (30 trials). The developed BCI system can be used to provide grasping assistance to ALS patients.

Towards a mechanistic approach for the development of non-invasive brain-computer interfaces for motor rehabilitation.

Brain-computer interfaces (BCIs) designed for motor rehabilitation use brain signals associated with motor-processing states to guide neuroplastic changes in a state-dependent manner. These technologies are uniquely positioned to induce targeted and functionally relevant plastic changes in the human motor nervous system. However, while several studies have shown that BCI-based neuromodulation interventions may improve motor function in patients with lesions in the central nervous system, the neurophysiological structures and processes targeted with the BCI interventions have not been identified. In this review, we first summarize current knowledge of the changes in the central nervous system associated with learning new motor skills. Then, we propose a classification of current BCI paradigms for plasticity induction and motor rehabilitation based on the expected neural plastic changes promoted. This classification proposes four paradigms based on two criteria: the plasticity induction methods and the brain states targeted. The existing evidence regarding the brain circuits and processes targeted with these different BCIs is discussed in detail. The proposed classification aims to serve as a starting point for future studies trying to elucidate the underlying plastic changes following BCI interventions.

Neuropsychological and neurophysiological aspects of brain-computer-interface (BCI) control in paralysis.

Brain-computer interfaces (BCIs) aim to help paralysed patients to interact with their environment by controlling external devices using brain activity, thereby bypassing the dysfunctional motor system. Some neuronal disorders, such as amyotrophic lateral sclerosis (ALS), severely impair the communication capacity of patients. Several invasive and non-invasive brain-computer interfaces (BCIs), most notably using electroencephalography (EEG), have been developed to provide a means of communication to paralysed patients. However, except for a few reports, all available BCI literature for the paralysed (mostly ALS patients) describes patients with intact eye movement control, i.e. patients in a locked-in state (LIS) but not a completely locked-in state (CLIS). In this article we will discuss: (1) the fundamental neuropsychological learning factors and neurophysiological factors determining BCI performance in clinical applications; (2) the difference between LIS and CLIS; (3) recent development in BCIs for communication with patients in the completely locked-in state; (4) the effect of BCI-based communication on emotional well-being and quality of life; and (5) the outlook and the methodology needed to provide a means of communication for patients who have none. Thus, we present an overview of available studies and recent results and try to anticipate future developments which may open new doors for BCI communication with the completely paralysed.

Generative Adversarial Networks-Based Data Augmentation for Brain-Computer Interface.

The performance of a classifier in a brain-computer interface (BCI) system is highly dependent on the quality and quantity of training data. Typically, the training data are collected in a laboratory where the users perform tasks in a controlled environment. However, users' attention may be diverted in real-life BCI applications and this may decrease the performance of the classifier. To improve the robustness of the classifier, additional data can be acquired in such conditions, but it is not practical to record electroencephalogram (EEG) data over several long calibration sessions. A potentially time- and cost-efficient solution is artificial data generation. Hence, in this study, we proposed a framework based on the deep convolutional generative adversarial networks (DCGANs) for generating artificial EEG to augment the training set in order to improve the performance of a BCI classifier. To make a comparative investigation, we designed a motor task experiment with diverted and focused attention conditions. We used an end-to-end deep convolutional neural network for classification between movement intention and rest using the data from 14 subjects. The results from the leave-one subject-out (LOO) classification yielded baseline accuracies of 73.04% for diverted attention and 80.09% for focused attention without data augmentation. Using the proposed DCGANs-based framework for augmentation, the results yielded a significant improvement of 7.32% for diverted attention ( ) and 5.45% for focused attention ( ). In addition, we implemented the method on the data set IVa from BCI competition III to distinguish different motor imagery tasks. The proposed method increased the accuracy by 3.57% ( ). This study shows that using GANs for EEG augmentation can significantly improve BCI performance, especially in real-life applications, whereby users' attention may be diverted.

Real-time neurofeedback is effective in reducing diversion of attention from a motor task in healthy individuals and patients with amyotrophic lateral sclerosis.

OBJECTIVE: The performance of brain-computer interface (BCI) systems is influenced by the user's mental state, such as attention diversion. In this study, we propose a novel online BCI system able to adapt with variations in the users' attention during real-time movement execution. APPROACH: Electroencephalography signals were recorded from healthy participants and patients with Amyotrophic Lateral Sclerosis while attention to the target task (a dorsiflexion movement) was drifted using an auditory oddball task. For each participant, the selected channels, classifiers and features from a training data set were used in the online phase to predict the attention status. MAIN RESULTS: For both healthy controls and patients, feedback to the user on attentional status reduced the amount of attention diversion. SIGNIFICANCE: The findings presented here demonstrate successful monitoring of the users' attention in a fully online BCI system, and further, that real-time neurofeedback on the users' attention state can be implemented to focus the attention of the user back onto the main task.

Dynamics of movement-related cortical potentials and sensorimotor oscillations during palmar grasp movements.

Movement-related cortical potentials (MRCP) and sensorimotor oscillatory electroencephalographic (EEG) activity (event-related desynchronization/synchronization-ERD/ERS) provide complementary information of the associated motor activity. The aim of this study was to provide comparative spatio-temporal analysis of both EEG phenomena associated with palmar grasping motions including hand opening and closing phases. Nine healthy participants were instructed to perform self-paced, right hand grasping movements. EEG was recorded from 28 sites synchronous with electromyography (EMG) of wrist/fingers extensors and flexors. Statistical analysis of the EEG data revealed significant differences (p < .05) between the idle state (baseline) and motor preparation/execution periods in majority of recorded channels. The earliest statistical significance in MRCPs was observed for channel FC3 at -460.9 ms, while the earliest significant ERD was observed at 164.1 ms for channel C3. MRCP and ERD/ERS topographies in our study are in line with the results of previous studies comparing MRCP and ERD/ERS spatio-temporal patterns during upper limb movements, however, results of our study show that MRCP significant differences compared to the baseline appear in most channels earlier than ERD (on average 613.6 ± 191.5 ms earlier). This implies an advantage of MRCP signals for grasping movements' prediction, which is in contrast to previous reports. Moreover, combined spatio-temporal information on MRCP and ERD/ERS presented in this paper may serve for future optimization of grasp movement prediction/detection hybrid algorithms in the context of restorative brain-computer interface technology.

A software for testing and training visuo-motor coordination for upper limb control.

BACKGROUND: Developing methods to accelerate improvements in motor function are welcomed in clinical practice. Therefore, the aim of this study is to describe changes in brain activity related to the execution of motor tasks implemented on a software - the NeuroMaze - developed specifically to stimulate speed-accuracy tradeoff. NEW METHOD: The NeuroMaze was tested in eleven young and healthy individuals in a single experimental session. The tasks consisted in moving a square appearing on the monitor by holding and dragging it with a mouse across paths of different widths (wide [2 cm] vs intermediate [1.5 cm] vs narrow [1 cm] widths). The mouse cursor speed and scalp electroencephalography (EEG) from the frontal, somatosensory and motor areas were recorded. RESULTS: The mouse speed is reduced by 15 ±â€¯6% and 48 ±â€¯7% from the wide to the intermediate and narrow paths respectively (p < 0.005). Moreover, there was a greater beta EEG relative power in the narrow path in the frontal area of the brain when compared to the wide path (p < 0.05). Similarly, the narrow path reduced the gamma EEG relative power in motor/sensorimotor areas when compared to the wide path (p < 0.05). COMPARISON WITH EXISTING METHODS: The NeuroMaze is introduced as a method to elicit speed-accuracy tradeoff, and the authors are not aware of specific methods to establish fair comparisons. CONCLUSION: The NeuroMaze creates conditions to stimulate brain areas related to motor planning, sensory feedback and motor execution using speed-accuracy tradeoff contexts. Therefore, the NeuroMaze may induce adaptations in patients undergoing upper limb rehabilitation.

Continuous 2D control via state-machine triggered by endogenous sensory discrimination and a fast brain switch.

OBJECTIVE: Brain computer interfacing (BCI) is a promising method to control assistive systems for patients with severe disabilities. Recently, we have presented a novel BCI approach that combines an electrotactile menu and a brain switch, which allows the user to trigger many commands robustly and efficiently. However, the commands are timed to periodic tactile cues and this may challenge online control. In the present study, therefore, we implemented and evaluated a novel approach for online closed-loop control using the proposed BCI. APPROACH: Eleven healthy subjects used the novel method to move a cursor in a 2D space. To assure robust control with properly timed commands, the BCI was integrated within a state machine allowing the subject to start the cursor movement in the selected direction and asynchronously stop the cursor. The brain switch was controlled using motor execution (ME) or imagery (MI) and the menu implemented four (straight movements) or eight commands (straight and diagonal movements). MAIN RESULTS: The results showed a high completion rate of a target hitting task (~97% and ~92% for ME and MI, respectively), with a small number of collisions, when four-channel control was used. There was no significant difference in outcome measures between MI and ME, and performance was similar for four and eight commands. SIGNIFICANCE: These results demonstrate that the novel state-based scheme driven by a robust BCI can be successfully utilized for online control. Therefore, it can be an attractive solution for providing the user an online-control interface with many commands, which is difficult to achieve using classic BCI solutions.

Classification of Movement Preparation Between Attended and Distracted Self-Paced Motor Tasks.

OBJECTIVE: Brain-computer interface (BCI) systems aim to control external devices by using brain signals. The performance of these systems is influenced by the user's mental state, such as attention. In this study, we classified two attention states to a target task (attended and distracted task level) while attention to the task is altered by one of three types of distractors. METHODS: A total of 27 participants were allocated into three experimental groups and exposed to one type of distractor. An attended condition that was the same across the three groups comprised only the main task execution (self-paced dorsiflexion) while the distracted condition was concurrent execution of the main task and an oddball task (dual-task condition). Electroencephalography signals were recorded from 28 electrodes to classify the two attention states of attended or distracted task conditions by extracting temporal and spectral features. RESULTS: The results showed that the ensemble classification accuracy using the combination of temporal and spectral features (spectro-temporal features, 82.3 ± 2.7%) was greater than using temporal (69 ± 2.2%) and spectral (80.3 ± 2.6%) features separately. The classification accuracy was computed using a combination of different channel locations, and it was demonstrated that a combination of parietal and centrally located channels was superior for classification of two attention states during movement preparation (parietal channels: 84.6 ± 1.3%, central and parietal channels: 87.2 ± 1.5%). CONCLUSION: It is possible to monitor the users' attention to the task for different types of distractors. SIGNIFICANCE: It has implications for online BCI systems where the requirement is for high accuracy of intention detection.

Sensory Stimulation Training for BCI System Based on Somatosensory Attentional Orientation.

In this study, we propose a sensory stimulation training (SST) approach to improve the performance of a brain-computer interface (BCI) based on somatosensory attentional orientation (SAO). In this BCI, subjects imagine the tactile sensation and maintain the attention on the corresponding hand as if there was a tactile stimulus on the wrist skin. Twenty BCI naïve subjects were recruited and randomly divided into a Control-Group and an SST-Group. In the Control-Group, subjects performed left hand and right hand SAO tasks in six consecutive runs (with 40 trials in each run), divided into three blocks with each having two runs. For the SST-Group, two runs included real tactile stimulation to the left or right hand (SST training block), between the first two (Pre-SST block) and the last two SAO runs (Post-SST block). Results showed that the SST-Group had a significantly improved performance of 9.4% between the last block and the first block after SST training (F(2,18) = 11.11, p = 0.0007); in contrast, no significant difference was found in the Control-Group between the first, second, and the last block (F(2,18) = 2.07, p = 0.1546), indicating no learning effect. The tactile sensation-induced oscillatory dynamics were similar to those induced by SAO. In the SST-Group, R2 discriminative information was enhanced around the somatosensory cortex due to the real sensory stimulation as compared with that in the Control-Group. Since the SAO mental task is inherently an internal process, the proposed SST method is meant as an adjuvant to SAO to facilitate subjects in achieving an initial SAO-based BCI control.

Brain state-dependent stimulation boosts functional recovery following stroke.

OBJECTIVE: Adjuvant protocols devised to enhance motor recovery in subacute stroke patients have failed to show benefits with respect to classic therapeutic interventions. Here, we evaluate the efficacy of a novel brain state-dependent intervention based on known mechanisms of memory and learning that is integrated as part of the weekly rehabilitation program in subacute stroke patients. METHODS: Twenty-four hospitalized subacute stroke patients were randomly assigned to 2 intervention groups: (1) the associative group received 30 pairings of a peripheral electrical nerve stimulus (ES) such that the generated afferent volley arrived precisely during the most active phase of the motor cortex as patients attempted to perform a movement; and (2) in the control group, the ES intensity was too low to generate a stimulation of the nerve. Functional (including the lower extremity Fugl-Meyer assessment [LE-FM; primary outcome measure]) and neurophysiological (changes in motor evoked potentials [MEPs]) assessments were performed prior to and following the intervention period. RESULTS: The associative group significantly improved functional recovery with respect to the control group (median [interquartile range] LE-FM improvement = 6.5 [3.5-8.25] and 3 [0.75-3], respectively; p = 0.029). Significant increases in MEP amplitude were seen following all sessions in the associative group only (p ≤ 0.006). INTERPRETATION: This is the first evidence of a clinical effect of a neuromodulatory intervention in the subacute phase of stroke. This was evident with relatively few repetitions in comparison to available techniques, making it a clinically viable approach. The results indicate the potential of the proposed neuromodulation system in daily clinical routine for stroke rehabilitation. ANN NEUROL 2019;85:84-95.

Delayed muscle onset soreness in the gastrocnemius muscle attenuates the spinal contribution to interlimb communication.

PURPOSE: Delayed onset muscle soreness (DOMS) has been shown to induce changes in muscle activity during walking. The aim of this study was to elucidate whether DOMS also affects interlimb communication during walking by investigating its effect on short-latency crossed responses (SLCRs). METHODS: SLCRs were elicited in two recording sessions by electrically stimulating the tibial nerve of the ipsilateral leg, and quantified in the contralateral gastrocnemius muscle. The second recording session occurred 24-36 h after the participants (n = 11) performed eccentric exercises with the ipsilateral calf. RESULTS: DOMS caused a decreased magnitude of the spinally mediated component of the SLCR in the contralateral gastrocnemius medialis. CONCLUSIONS: The results of the current study provide insight on the relationship between pain and motor control. Muscle pain affects the spinal pathway mediating interlimb communication, which might result in a reduced ability to maintain dynamical stability during walking.

Evidence for a Supraspinal Contribution to the Human Crossed Reflex Response During Human Walking.

In humans, an ipsilateral tibial nerve (iTN) stimulation elicits short-latency-crossed-responses (SLCR) comprised of two bursts in the contralateral gastrocnemius lateralis (cGL) muscle. The average onset latency has been reported to be 57-69 ms with a duration of 30.4 ± 6.6 ms. The aim of this study was to elucidate if a transcortical pathway contributes to the SLCR. In Experiment 1 (n = 9), single pulse supra-threshold transcranial magnetic stimulation (supraTMS) was applied alone or in combination with iTN stimulation (85% of the maximum M-wave) while participants walked on a treadmill (delay between the SLCR and the motor evoked potentials (MEP) varied between -30 and 200 ms). In Experiment 2 (n = 6), single pulse sub-threshold TMS (subTMS) was performed and the interstimulus interval (ISI) varied between 0-30 ms. In Experiment 3, somatosensory evoked potentials (SEPs) were recorded during the iTN stimulation to quantify the latency of the resulting afferent volley at the cortical level. SLCRs and MEPs in cGL occurred at 63 ± 6 ms and 29 ± 2 ms, respectively. The mean SEP latency was 30 ± 3 ms. Thus, a transcortical pathway could contribute no earlier than 62-69 ms (SEP+MEP+central-processing-delay) after iTN stimulation. Combined iTN stimulation and supraTMS resulted in a significant MEP extra-facilitation when supraTMS was timed so that the MEP would coincide with the late component of the SLCR, while subTMS significantly depressed this component. This is the first study that demonstrates the existence of a strong cortical control on spinal pathways mediating the SLCR. This likely serves to enhance flexibility, ensuring that the appropriate output is produced in accord with the functional demand.

A Multi-Class BCI Based on Somatosensory Imagery.

In this paper, we investigated the performance of a multi-class brain-computer interface (BCI). The BCI system is based on the concept of somatosensory attentional orientation (SAO), in which the user shifts and maintains somatosensory attention by imagining the sensation of tactile stimulation of a body part. At the beginning of every trial, a vibration stimulus (200 ms) informed the subjects to prepare for the task. Four SAO tasks were performed following randomly presented cues: SAO of the left hand (SAO-LF), SAO of the right hand (SAO-RT), bilateral SAO (SAO-BI), and SAO suppressed or idle state (SAO-ID). Analysis of the event-related desynchronization and synchronization (ERD/ERS) in the EEG indicated that the four SAO tasks had different somatosensory cortical activation patterns. SAO-LF and SAO-RT exhibited stronger contralateral ERD, whereas bilateral ERD activation was indicative of SAO-BI, and bilateral ERS activation was associated with SAO-ID. By selecting the frequency bands and/or optimal classes, classification accuracy of the system reached 85.2%±11.2% for two classes, 69.5%±16.2% for three classes, and 55.9%±15.8% for four classes. The results validated a multi-class BCI system based on SAO, on a single trial basis. Somatosensory attention to different body parts induces diverse oscillatory dynamics within the somatosensory area of the brain, and the proposed SAO paradigm provided a new approach for a multiple-class BCI that is potentially stimulus independent.