Hand Movement Imagination Research Articles

A multiple session dataset of NIRS recordings from stroke patients controlling brain–computer interface

This paper presents an open dataset of over 50 hours of near infrared spectroscopy (NIRS) recordings. Fifteen stroke patients completed a total of 237 motor imagery brain–computer interface (BCI) sessions. The BCI was controlled by imagined hand movements; visual feedback was presented based on the real–time data classification results. We provide the experimental records, patient demographic profiles, clinical scores (including ARAT and Fugl–Meyer), online BCI performance, and a simple analysis of hemodynamic response. We assume that this dataset can be useful for evaluating the effectiveness of various near–infrared spectroscopy signal processing and analysis techniques in patients with cerebrovascular accidents.

Enhancing poststroke hand movement recovery: Efficacy of RehabSwift, a personalized brain-computer interface system.

This study explores the efficacy of our novel and personalized brain-computer interface (BCI) therapy, in enhancing hand movement recovery among stroke survivors. Stroke often results in impaired motor function, posing significant challenges in daily activities and leading to considerable societal and economic burdens. Traditional physical and occupational therapies have shown limitations in facilitating satisfactory recovery for many patients. In response, our study investigates the potential of motor imagery-based BCIs (MI-BCIs) as an alternative intervention. In this study, MI-BCIs translate imagined hand movements into actions using a combination of scalp-recorded electrical brain activity and signal processing algorithms. Our prior research on MI-BCIs, which emphasizes the benefits of proprioceptive feedback over traditional visual feedback and the importance of customizing the delay between brain activation and passive hand movement, led to the development of RehabSwift therapy. In this study, we recruited 12 chronic-stage stroke survivors to assess the effectiveness of our solution. The primary outcome measure was the Fugl-Meyer upper extremity (FMA-UE) assessment, complemented by secondary measures including the action research arm test, reaction time, unilateral neglect, spasticity, grip and pinch strength, goal attainment scale, and FMA-UE sensation. Our findings indicate a remarkable improvement in hand movement and a clinically significant reduction in poststroke arm and hand impairment following 18 sessions of neurofeedback training. The effects persisted for at least 4 weeks posttreatment. These results underscore the potential of MI-BCIs, particularly our solution, as a prospective tool in stroke rehabilitation, offering a personalized and adaptable approach to neurofeedback training.

A Phase-based EEG Epoch Selection Method for Decoding Bi-directional Hand Movement Imagination in Stroke Patients.

Electroencephalogram (EEG) based non-invasive Brain Computer Interface (BCI) system is gaining significant attention as a promising solution for stroke rehabilitation. Accurate selection of informative EEG time segment, that accommodates the specific neural activity patterns associated with the underlying mental task can help to improve the efficacy of the BCI system. In this work, we propose a phase-based EEG epoch selection algorithm to extract the discriminative EEG time segment corresponding to bi-directional hand motor imagery. The imagined center-out hand movement in two directions is decoded using the selected epoch of the EEG, recorded from 16 stroke patients with hemiparesis and specifically hand weakness. Phase Lock Value (PLV) EEG features extracted from the selected EEG epoch is used as discriminative feature for binary classification of imagined hand movement direction using Linear Discriminant Analysis. The use of selected EEG epoch yielded an improvement of 11.5% and 11.7% in the average direction classification accuracy of calibration and feedback session data respectively, compared to the baseline method employing the whole EEG signal. In addition to improvement in decoding accuracy, the epoch selection also yielded an average Information Transfer Rate (ITR) of 39.8±24.6 bits per minute, which is 86% improvement compared to the baseline method.Clinical Relevance- The proposed Motor Imagery (MI)-BCI system may be of clinical relevance as an active rehabilitation tool for stroke-affected patients, to enhance neural plasticity and recovery of centre-out activities of affected hand and forms a strong platform for MI-BCI coupled with exoskeletons or prosthesis rehabilitation.

Electrical activity of the brain during the imagination of purposeful hand movements in persons playing stringed and keyboard musical instruments

The imagination of purposeful movements and their implementation activates the same areas of the cerebral cortex, which is actively used in the creation of the brain computer interface and its implementation in the field of healthcare in the form of neuro-controlled exoskeletal limbs and robots that perform complex medical manipulations using the human brain. Playing musical instruments contributes to the development of upper limb motor skills and the development of interneuronal connections of the cerebral cortex. The aim of the work was to study brain activity when imagining purposeful movement in persons playing musical instruments. Brain activity was measured using electroencephalography. Interhemispheric asymmetry was determined using a complex method to determine the leading hemisphere. Because of the conducted research, it was found that the skills of playing musical instruments, even in the absence of sensory afferentation from the periphery during the imaginary initiation of movement, contribute to a stronger combined activation of the sensory and motor areas of the cortex.

Dependence of Brain–Computer Interface Control Training on Personality Traits

Personality traits (PTs) are predictors of the success of control of brain-computer interfaces (BCIs); however, it is unknown how the PTs that are optimal for BCI control changes during training. The paper for the first time analyzes the correlations between PTs and the accuracy of the classification (AC) of brain states in imagining the movements of the hands, feet, and locomotion during 10-day training of ten volunteers in BCI control. In the first 3 days of training, the AC is higher for more stressed and anxious volunteers; in the last days, for calmer ones. In the middle of the training period, AC is higher in low-demonstrativeness persons, it is more pronounced when imagining foot movements. Correlations of low demonstrativeness, as well as of foresight and self-control with AC when imagining foot movements are revealed significantly more often than when imagining hand movements and locomotions. During almost the entire period of training, AC with locomotion imagination is higher in individualists. The results make it possible to propose individually-oriented recommendations for the use of BCI based on the imagination of movements for the rehabilitation of patients with motor disorders.

Mental Strategies in a P300-BCI: Visuomotor Transformation Is an Option.

Currently, P300-BCIs are mostly used for spelling tasks, where the number of commands is equal to the number of stimuli that evoke event-related potentials (ERPs). Increasing this number slows down the BCI operation because each stimulus has to be presented several times for better classification. Furthermore, P300 spellers typically do not utilize potentially useful imagery-based approaches, such as the motor imagery successfully practiced in motor rehabilitation. Here, we tested a P300-BCI with a motor-imagery component. In this BCI, the number of commands was increased by adding mental strategies instead of increasing the number of targets. Our BCI had only two stimuli and four commands. The subjects either counted target appearances mentally or imagined hand movements toward the targets. In this design, the motor-imagery paradigm enacted a visuomotor transformation known to engage cortical and subcortical networks participating in motor control. The operation of these networks suffers in neurological conditions such as stroke, so we view this BCI as a potential tool for the rehabilitation of patients. As an initial step toward the development of this clinical method, sixteen healthy participants were tested. Consistent with our expectation that mental strategies would result in distinct EEG activities, ERPs were different depending on whether subjects counted stimuli or imagined movements. These differences were especially clear in the late ERP components localized in the frontal and centro-parietal regions. We conclude that (1) the P300 paradigm is suitable for enacting visuomotor transformations and (2) P300-based BCIs with multiple mental strategies could be used in applications where the number of possible outputs needs to be increased while keeping the number of targets constant. As such, our approach adds to both the development of versatile BCIs and clinical approaches to rehabilitation.

Decoding trajectories of imagined hand movement using electrocorticograms for brain–machine interface

Objective. Reaching hand movement is an important motor skill actively examined in the brain–computer interface (BCI). Among the various components of movement analyzed is the hand’s trajectory, which describes the hand’s continuous positions in three-dimensional space. While a large body of studies have investigated the decoding of real movements and the reconstruction of real hand movement trajectories from neural signals, fewer studies have attempted to decode the trajectory of the imagined hand movement. To develop BCI systems for patients with hand motor dysfunctions, the systems essentially have to achieve movement-free control of external devices, which is only possible through successful decoding of purely imagined hand movement. Approach. To achieve this goal, this study used a machine learning technique (i.e. the variational Bayesian least square) to analyze the electrocorticogram (ECoG) of 18 epilepsy patients obtained from when they performed movement execution (ME) and kinesthetic movement imagination (KMI) of the reach-and-grasp hand action. Main results. The variational Bayesian decoding model was able to successfully predict the imagined trajectories of the hand movement significantly above the chance level. The Pearson’s correlation coefficient between the imagined and predicted trajectories was 0.3393 and 0.4936 for the KMI (KMI trials only) and MEKMI paradigm (alternating trials of ME and KMI), respectively. Significance. This study demonstrated a high accuracy of prediction for the trajectories of imagined hand movement, and more importantly, a higher decoding accuracy of the imagined trajectories in the MEKMI paradigm compared to the KMI paradigm solely.

Direction decoding of imagined hand movements using subject-specific features from parietal EEG

Objective. Research on the decoding of brain signals to control external devices is rapidly emerging due to its versatile potential applications, including neuroprosthetic control and neurorehabilitation. Electroencephalogram (EEG)-based non-invasive brain–computer interface (BCI) systems decode brain signals to establish an augmented communication and control pathway between the brain and the computer. The development of an efficient BCI system requires accurate decoding of neural activity underlying the user’s intentions. This study investigates the directional tuning of EEG characteristics from the posterior parietal region, associated with bidirectional hand movement imagination or motor imagery (MI) in left and right directions. Approach. The imagined movement directions of the chosen hand were decoded using a combination of envelope and phase features derived from parietal EEGs of both hemispheres. The proposed algorithm uses wavelets for spectral decomposition, and discriminative subject-specific subband levels are identified based on Fisher analysis of envelope and phase features. The selected features from the discriminative subband levels are used to classify left and right MI directions of the hand using a support vector machine classifier. Furthermore, the performance of the proposed algorithm is evaluated by incorporating a maximum-variance-based EEG time bin selection algorithm. Main results. With the time bin selection approach using subject-specific features, the proposed algorithm yielded an average left vs right MI direction decoding accuracy of 73.33% across 15 healthy subjects. In addition, the decoding accuracy offered by the phase features was higher than that of the envelope features, indicating the importance of phase features in MI kinematics decoding. Significance. The results reveal the significance of the parietal EEG in decoding of imagined kinematics and open new possibilities for future BCI research.

Low Frequency Brain Oscillations during the execution and imagination of simple hand movements for Brain-Computer Interface applications.

Low Frequency Brain Oscillations (LFOs) are brief periods of oscillatory activity in delta and lower theta band that appear at motor cortical areas before and around movement onset. It has been shown that LFO power decreases in post-stroke patients and re-emerges with motor functional recovery. To date, LFOs have not yet been explored during the motor execution (ME) and imagination (MI) of simple hand movements, often used in BCI-supported motor rehabilitation protocols post-stroke. This study aims at analyzing the LFOs during the ME and MI of the finger extension task in a sample of 10 healthy subjects and 2 stroke patients in subacute phase. The results showed that LFO power peaks occur in the preparatory phase of both ME and MI tasks on the sensorimotor channels in healthy subjects and their alterations in stroke patients. Clinical Relevance- Results suggest that LFOs could be explored as biomarker of the motor function recovery in rehabilitative protocols based on the movement imagination.

Neuromotor prosthetic to treat stroke-related paresis: N-of-1 trial

BackgroundFunctional recovery of arm movement typically plateaus following a stroke, leaving chronic motor deficits. Brain-computer interfaces (BCI) may be a potential treatment for post-stroke deficitsMethodsIn this n-of-1 trial (NCT03913286), a person with chronic subcortical stroke with upper-limb motor impairment used a powered elbow-wrist-hand orthosis that opened and closed the affected hand using cortical activity, recorded from a percutaneous BCI comprised of four microelectrode arrays implanted in the ipsilesional precentral gyrus, based on decoding of spiking patterns and high frequency field potentials generated by imagined hand movements. The system was evaluated in a home setting for 12 weeksResultsRobust single unit activity, modulating with attempted or imagined movement, was present throughout the precentral gyrus. The participant acquired voluntary control over a hand-orthosis, achieving 10 points on the Action Research Arm Test using the BCI, compared to 0 without any device, and 5 using myoelectric control. Strength, spasticity, the Fugl-Meyer scores improved.ConclusionsWe demonstrate in a human being that ensembles of individual neurons in the cortex overlying a chronic supratentorial, subcortical stroke remain active and engaged in motor representation and planning and can be used to electrically bypass the stroke and promote limb function. The participant’s ability to rapidly acquire control over otherwise paralyzed hand opening, more than 18 months after a stroke, may justify development of a fully implanted movement restoration system to expand the utility of fully implantable BCI to a clinical population that numbers in the tens of millions worldwide.

Effect of long-term paired associative stimulation on the modulation of cortical sensorimotor oscillations after spinal cord injury

Study designA prospective interventional case series.ObjectivesTo explore changes in the modulation of cortical sensorimotor oscillations after long-term paired associative stimulation (PAS) in participants with spinal cord injury (SCI).SettingBioMag Laboratory, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.MethodsFive patients with chronic incomplete SCI received unilateral spinal PAS to upper limb for 16–22 days. Changes in the modulation of sensorimotor oscillations in response to tactile stimulus and active and imaginary hand movements were assessed with magnetoencephalography recorded before and after the intervention.ResultsPAS restored the modulation of sensorimotor oscillations in response to active hand movement in four patients, whereas the modulation following tactile stimulation remained unaltered. The observed change was larger in the hemisphere that received PAS and preceded the clinical effect of the intervention.ConclusionsLong-term spinal PAS treatment, which enhances the motor functions of SCI patients, also restores the modulation of cortical sensorimotor oscillations.

FMRI-based validation of continuous-wave fNIRS of supplementary motor area activation during motor execution and motor imagery

Compared to functional magnetic resonance imaging (fMRI), functional near infrared spectroscopy (fNIRS) has several advantages that make it particularly interesting for neurofeedback (NFB). A pre-requisite for NFB applications is that with fNIRS, signals from the brain region of interest can be measured. This study focused on the supplementary motor area (SMA). Healthy older participants (N = 16) completed separate continuous-wave (CW-) fNIRS and (f)MRI sessions. Data were collected for executed and imagined hand movements (motor imagery, MI), and for MI of whole body movements. Individual anatomical data were used to (i) define the regions of interest for fMRI analysis, to (ii) extract the fMRI BOLD response from the cortical regions corresponding to the fNIRS channels, and (iii) to select fNIRS channels. Concentration changes in oxygenated (Delta [HbO]) and deoxygenated (Delta [HbR]) hemoglobin were considered in the analyses. Results revealed subtle differences between the different MI tasks, indicating that for whole body MI movements as well as for MI of hand movements Delta [HbR] is the more specific signal. Selection of the fNIRS channel set based on individual anatomy did not improve the results. Overall, the study indicates that in terms of spatial specificity and task sensitivity SMA activation can be reliably measured with CW-fNIRS.

Comparing between Different Sets of Preprocessing, Classifiers, and Channels Selection Techniques to Optimise Motor Imagery Pattern Classification System from EEG Pattern Recognition

The ability to control external devices through thought is increasingly becoming a reality. Human beings can use the electrical signals of their brain to interact or change the surrounding environment and more. The development of this technology called brain-computer interface (BCI) will increasingly allow people with motor disabilities to communicate or use assistive devices to walk, manipulate objects and communicate. Using data from the PhysioNet database, this study implemented a pattern classification system for use in a BCI on 109 healthy volunteers during real movement activities and motor imagery recorded by 64-channels electroencephalography (EEG) system. Different classifiers such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Trees (TREE) were applied on different combinations of EEG channels. Starting from two channels (C3, C4 and CP3 and CP4) positioned on the contralateral and ipsilateral sensorimotor cortex, the Region of Interest (RoI) centred on C3/Cp3 and C4/Cp4 and, finally, a data-driven automatic channels selection was tested to explore the best channel combination able to increase the classification accuracy. The results showed that the proposed automatic channels selection was able to significantly improve the performance of each classifier achieving 98% of accuracy for classification of real and imagined hand movement (sensitivity = 97%, specificity = 99%, AUC = 0.99) by SVM. While the accuracy of the classification between the imagery of hand and foot movements was 91% (sensitivity = 87%, specificity = 86%, AUC = 0.93) also with SVM. In the proposed approach, the data-driven automatic channels selection outperforms classical a priori channel selection models such as C3/C4, Cp3/Cp4, or RoIs around those channels with the utmost accuracy to help remove the boundaries of human communication and improve the quality of life of people with disabilities.

Sequential neural activity in sensorimotor area and mirror neural system for graded mirror therapy with imagined hand movements.

Graded motor imagery (GMI) therapy is a neural rehabilitative physiotherapy that has been shown to alleviate the severity of complex regional pain syndrome, phantom limb pain and disability. To identify neural networks associated with the use of graded mirror therapy (MT) while imagining hand movements. We made a block-design functional magnetic resonance imaging study of MT included three experiments: (1) immobile unimanual MT (IU-MT), in which the right hand flexed and made a fist, which then remained immobile; (2) mobilization unimanual MT (MU-MT), in which the right hand performed a flexion-extension movement; and (3) mobilization bimanual MT (MB-MT), in which both hands performed a flexion-extension movement. When subjects started their hand movements, they gazed at the mirror and imagined the same movement behind the mirror. We discovered that the sensorimotor area of the left brain, superior temporal gyrus/middle temporal gyrus (STG/MTG) of the right brain and visual areas were activated by IU-MT. In MU-MT, only the STG/MTG was activated. Furthermore, MB-UT mostly activated the sensorimotor area and STG of the right brain. However, there were no brain areas activated by MU-MT compared with IU-MT or MB-MT; but, MB-MT activated more motor areas than IU-MT. Importantly, we determined that the level of mirror imagery was negatively correlated with signals in the mirror neuron system (MNS) and positively related with the signals in the sensorimotor areas. We suggest that graded MT might be a sequential therapeutic program that can enhance the sensorimotor cortex. The MNS might have an initiating role in graded MT. Thus, there is the possibility that graded MT is a helpful treatment strategy for the rehabilitation of dysfunctional patients.

High accuracy decoding of motor imagery directions from EEG-based brain computer interface using filter bank spatially regularised common spatial pattern method

One of the important requirements of a practical Brain Computer Interface (BCI) system is the ability to establish multiple control commands corresponding to different kinematics of motor imagery. Most of the previous BCI-based motor imagery studies in literature focus on classifying left vs. right hand motor imagery from electroencephalogram (EEG) signals. Very few studies have reported decoding imagined hand movement kinematics from EEG-based BCI. The present study decodes the left vs. right directional information from the Motor Imagery (MI) of dominant hand movement using EEG-based BCI.The proposed method employs common spatial pattern (CSP) and its variants as features to decode imagined (motor imagery) bidirectional hand movements. The direction discriminability of these features are enhanced using the regularisation technique. Spatial regularisation based on electrode positions is also incorporated for comparison. The regularisation methods are applied on overlapped frequency bands and the results are compared. The classifications of extracted features are done using 5-fold cross-validation and Linear Discriminant Analysis (LDA). The study on 15 healthy subjects shows that filter bank-based spatially regularized CSP method (FBSRCSP) offers the highest average classification accuracy of 90% for decoding bidirectional motor imagery of hand movement. This is a substantial improvement in classification accuracy by 26.3% compared to the best method reported in literature on the same dataset.

Success of Hand Movement Imagination Depends on Personality Traits, Brain Asymmetry, and Degree of Handedness

Brain-computer interfaces (BCIs), based on motor imagery, are increasingly used in neurorehabilitation. However, some people cannot control BCI, predictors of this are the features of brain activity and personality traits. It is not known whether the success of BCI control is related to interhemispheric asymmetry. The study was conducted on 44 BCI-naive subjects and included one BCI session, EEG-analysis, 16PF Cattell Questionnaire, estimation of latent left-handedness, and of subjective complexity of real and imagery movements. The success of brain states recognition during imagination of left hand (LH) movement compared to the rest is higher in reserved, practical, skeptical, and not very sociable individuals. Extraversion, liveliness, and dominance are significant for the imagination of right hand (RH) movements in “pure” right-handers, and sensitivity in latent left-handers. Subjective complexity of real LH and of imagery RH movements correlates with the success of brain states recognition in the imagination of movement of LH compared to RH and depends on the level of handedness. Thus, the level of handedness is the factor influencing the success of BCI control. The data are supposed to be connected with hemispheric differences in motor control, lateralization of dopamine, and may be important for rehabilitation of patients after a stroke.

Multi-Session Influence of Two Modalities of Feedback and Their Order of Presentation on MI-BCI User Training

By performing motor-imagery tasks, for example, imagining hand movements, Motor-Imagery based Brain-Computer Interfaces (MI-BCIs) users can control digital technologies, for example, neuroprosthesis, using their brain activity only. MI-BCI users need to train, usually using a unimodal visual feedback, to produce brain activity patterns that are recognizable by the system. The literature indicates that multimodal vibrotactile and visual feedback is more effective than unimodal visual feedback, at least for short term training. However, the multi-session influence of such multimodal feedback on MI-BCI user training remained unknown, so did the influence of the order of presentation of the feedback modalities. In our experiment, 16 participants trained to control a MI-BCI during five sessions with a realistic visual feedback and five others with both a realistic visual feedback and a vibrotactile one. training benefits from a multimodal feedback, in terms of performances and self-reported mindfulness. There is also a significant influence of the order presentation of the modality. Participants who started training with a visual feedback had higher performances than those who started training with a multimodal feedback. We recommend taking into account the order of presentation for future experiments assessing the influence of several modalities of feedback.

Rule-Based EEG Classifier Utilizing Local Entropy of Time–Frequency Distributions

Electroencephalogram (EEG) signals are known to contain signatures of stimuli that induce brain activities. However, detecting these signatures to classify captured EEG waveforms is one of the most challenging tasks of EEG analysis. This paper proposes a novel time–frequency-based method for EEG analysis and characterization implemented in a computer-aided decision-support system that can be used to assist medical experts in interpreting EEG patterns. The computerized method utilizes EEG spectral non-stationarity, which is clearly revealed in the time–frequency distributions (TFDs) of multicomponent signals. The proposed algorithm, which is based on the modification of the Rényi entropy, called local or short-term Rényi entropy (STRE), was upgraded with a blind component separation procedure and instantaneous frequency (IF) estimation. The method was applied to EEGs of both forward and backward movements of the left and right hands, as well as to EEGs of imagined hand movements, which were captured by a 19-channel EEG recording system. The obtained results show that in a given virtual instrument, the proposed methods efficiently distinguish between real and imagined limb movements by considering their signatures in terms of the dominant EEG component’s IFs at the specified subset of EEG channels (namely, F3, F4, F7, F8, T3, and T4). Furthermore, computing the number of EEG signal components, their extraction, and IF estimation provide important information that shows potential to enhance existing clinical diagnostic techniques for detecting the intensity, location, and type of brain function abnormalities in patients with neurological motor control disorders.

Experimenters' Influence on Mental-Imagery based Brain-Computer Interface User Training

Context: Motor Imagery based Brain-Computer Interfaces (MI-BCIs) enable their users to interact with digital technologies, e.g., neuroprosthesis, by performing motor imagery tasks only, e.g., imagining hand movements, while their brain activity is recorded. To control MI-BCIs, users must train to control their brain activity. During such training, experimenters have a fundamental role, e.g., they motivate participants. However, their influence had never been formally assessed for MI-BCI user training. In other fields, e.g., social psychology, experimenters gender was found to influence experimental outcomes, e.g., behavioural or neurophysiological measures. Objective: Our aim was to evaluate if the experimenters gender influenced MI-BCI user training outcomes, i.e., performances and user-experience. Methods: We performed an experiment involving 6 experimenters (3 women) each training 5 women and 5 men (60 participants) to perform right versus left hand MI-BCI tasks over one session. We then studied the training outcomes, i.e., MI-BCI performances and user-experience, according to the experimenters’ and subjects’ gender. Results: A significant interaction between experimenters and participants’ gender was found on the evolution of trial-wise performances. Another interaction was found between participants tension and experimenters gender on the average performances. Conclusion: Experimenters gender could influence MI-BCI performances depending on participants gender and tension. Significance: Experimenters influence on MI-BCI user training outcomes should be better controlled, assessed and reported to further benefit from it while preventing any bias.

Multilayer extreme learning machine for hand movement prediction based on electroencephalography

Brain computer interface (BCI) technology connects humans with machines via electroencephalography (EEG). The mechanism of BCI is pattern recognition, which proceeds by feature extraction and classification. Various feature extraction and classification methods can differentiate human motor movements, especially those of the hand. Combinations of these methods can greatly improve the accuracy of the results. This article explores the performances of nine feature-extraction types computed by a multilayer extreme learning machine (ML-ELM). The proposed method was tested on different numbers of EEG channels and different ML-ELM structures. Moreover, the performance of ML-ELM was compared with those of ELM, Support Vector Machine and Naive Bayes in classifying real and imaginary hand movements in offline mode. The ML-ELM with discrete wavelet transform (DWT) as feature extraction outperformed the other classification methods with highest accuracy 0.98. So, the authors also found that the structures influenced the accuracy of ML-ELM for different task, feature extraction used and channel used.