Implementation Of Brain-computer Interface Research Articles

Classification of Motor Imagery Using Trial Extension in Spatial Domain with Rhythmic Components of EEG

Electrical activities of the human brain can be recorded with electroencephalography (EEG). To characterize motor imagery (MI) tasks for brain–computer interface (BCI) implementation is an easy and cost-effective tool. The MI task is represented by a short-time trial of multichannel EEG. In this paper, the signal of each channel of raw EEG is decomposed into a finite set of narrowband signals using a Fourier-transformation-based bandpass filter. Rhythmic components of EEG are represented by each of the narrowband signals that characterize the brain activities related to MI tasks. The subband signals are arranged to extend the dimension of the EEG trial in the spatial domain. The spatial features are extracted from the set of extended trials using a common spatial pattern (CSP). An optimum number of features are employed to classify the motor imagery tasks using an artificial neural network. An integrated approach with full-band and narrowband signals is implemented to derive discriminative features for MI classification. In addition, the subject-dependent parameter optimization scheme enhances the performance of the proposed method. The performance evaluation of the proposed method is obtained using two publicly available benchmark datasets (Dataset I and Dataset II). The experimental results in terms of classification accuracy (93.88% with Dataset I and 91.55% with Dataset II) show that it performs better than the recently developed algorithms. The enhanced MI classification accuracy is very much applicable in BCI implementation.

Source Aware Deep Learning Framework for Hand Kinematic Reconstruction Using EEG Signal.

The ability to reconstruct the kinematic parameters of hand movement using noninvasive electroencephalography (EEG) is essential for strength and endurance augmentation using exoskeleton/exosuit. For system development, the conventional classification-based brain-computer interface (BCI) controls external devices by providing discrete control signals to the actuator. A continuous kinematic reconstruction from EEG signal is better suited for practical BCI applications. The state-of-the-art multivariable linear regression (mLR) method provides a continuous estimate of hand kinematics, achieving a maximum correlation of up to 0.67 between the measured and the estimated hand trajectory. In this work, three novel source aware deep learning models are proposed for motion trajectory prediction (MTP). In particular, multilayer perceptron (MLP), convolutional neural network-long short-term memory (CNN-LSTM), and wavelet packet decomposition (WPD) for CNN-LSTM are presented. In addition, novelty in the work includes the utilization of brain source localization (BSL) [using standardized low-resolution brain electromagnetic tomography (sLORETA)] for the reliable decoding of motor intention. The information is utilized for channel selection and accurate EEG time segment selection. The performance of the proposed models is compared with the traditionally utilized mLR technique on the reach, grasp, and lift (GAL) dataset. The effectiveness of the proposed framework is established using the Pearson correlation coefficient (PCC) and trajectory analysis. A significant improvement in the correlation coefficient is observed when compared with the state-of-the-art mLR model. Our work bridges the gap between the control and the actuator block, enabling real-time BCI implementation.

Effects of Background Music on Mental Fatigue in Steady-State Visually Evoked Potential-Based BCIs

As a widely used brain-computer interface (BCI) paradigm, steady-state visually evoked potential (SSVEP)-based BCIs have the advantages of high information transfer rates, high tolerance for artifacts, and robust performance across diverse users. However, the incidence of mental fatigue from prolonged, repetitive stimulation is a critical issue for SSVEP-based BCIs. Music is often used as a convenient, non-invasive means of relieving mental fatigue. This study investigates the compensatory effect of music on mental fatigue through the introduction of different modes of background music in long-duration, SSVEP-BCI tasks. Changes in electroencephalography power index, SSVEP amplitude, and signal-to-noise ratio were used to assess participants' mental fatigue. The study's results show that the introduction of exciting background music to the SSVEP-BCI task was effective in relieving participants' mental fatigue. In addition, for continuous SSVEP-BCI tasks, a combination of musical modes that used soothing background music during the rest interval phase proved more effective in reducing users' mental fatigue. This suggests that background music can provide a practical solution for long-duration SSVEP-based BCI implementation.

A static paradigm based on illusion-induced VEP for brain-computer interfaces

Objective. Visual evoked potentials (VEPs) have been commonly applied in brain-computer interfaces (BCIs) due to their satisfactory classification performance recently. However, most existing methods with flickering or oscillating stimuli will induce visual fatigue under long-term training, thus restricting the implementation of VEP-based BCIs. To address this issue, a novel paradigm adopting static motion illusion based on illusion-induced visual evoked potential is proposed for BCIs to enhance visual experience and practicality. Approach. This study explored the responses to baseline and illusion tasks including the rotating-tilted-lines illusion and rotating-snakes illusion. The distinguishable features were examined between different illusions by analyzing the event-related potentials and amplitude modulation of evoked oscillatory responses. Main results. The illusion stimuli elicited VEPs in an early time window encompassing a negative component (N1) from 110 to 200 ms and a positive component (P2) between 210 and 300 ms. Based on the feature analysis, a filter bank was designed to extract discriminative signals. The task-related component analysis was used to evaluate the binary classification task performance of the proposed method. Then the highest accuracy of 86.67% was achieved with a data length of 0.6 s. Significance. The results of this study demonstrate that the static motion illusion paradigm has the feasibility of implementation and is promising for VEP-based BCI applications.

Implementation of BCI based semi-automated impact device for performing Impact Synchronous Modal Analysis

Current Impact Synchronous Modal Analysis (ISMA), an operational modal analysis technique incorporated with either manual impact hammer or Automated Phase Controlled Impact Device (APCID) faces challenges on its effectiveness and practicality in real industrial applications. This study utilizes a Brain Computer Interface (BCI) based portable semi-automated impact device with the aim to solve the current issues of ISMA and complement both the manual impact hammer and APCID. A low cost, portable and wireless Electroencephalogram (EEG) device is used with machine learning to predict impact time before the impact using manual impacts and integrated with APCID control to compensate for the behaviour randomness. This semi-automated impact device, also known as EEG-ISMA, provides an accurate, practical, time- and cost-effective solution in performing ISMA during operation with low mean error in impact time prediction, superior cyclic load component suppression capability, accurate modal parameters extraction and shorter time required.

Review on BCI Virtual Rehabilitation and Remote Technology Based on EEG for Assistive Devices

Virtual reality has been widely used in various industries, such as entertainment, communication, and healthcare, to mention a few. In the health industry, in combination with the brain–computer interfaces (BCIs), virtual reality could produce rehabilitation measures that may contribute novel strategies such as remote rehabilitation or telerehabilitation. The design and development of BCIs integrate different processes, including biosignals acquisition and processing, feature extraction and selection, classification of signals, and application of the technology to patients under rehabilitation treatments. This manuscript presents a literature review of the papers focused on the implementation of BCI and assistive technologies for remote rehabilitation based on virtual reality implementation. The purpose of this comprehensive review is to identify those studies that take advantage of virtual reality in combination with a biomedical technology to improve the performances of diverse rehabilitation processes. Various revisited studies provided a complete system for remote rehabilitation. These findings could lead to applying these models in diverse rehabilitation tasks.

A brain-computer interface with gamification in the Metaverse

This study contributes to our understanding of the Metaverse by presenting a case study of the implementation of brain-computer interface supported game-based engagement in a Virtual Environment (VE). In VE, individuals can communicate with anyone, anywhere, anytime, without any limits. This situation will increase the barrier-free living standards of disabled people in a more accessible environment. A virtual world of well-being awaits these individuals, primarily through gamified applications thanks to Brain-Computer Interfaces. Virtual environments in the Metaverse can be infinitely large, but the user's movement in a virtual reality (VR) environment is constrained by the natural environment. Locomotion has become a popular motion interface as it allows for full exploration of VE. In this study, the teleport method from locomotion methods was used. To teleport, the user selects the intended location using brain signals before being instantly transported to that location. Brain signals are decomposed into alpha, beta, and gamma bands. The features of each band signal in Time, frequency, and time-frequency domains are extracted. In this proposed method, the highest performance of binary classification was obtained in the frequency domain and the Alpha band. Signals in the alpha band were tested in the domains Time, Frequency, and Time-Frequency. Teleport operations are faster with Time and more stable with the frequency domain. However, the Hilbert-Huang Transform (HHT) method used in the Time-Frequency domain could not respond adequately to real-time applications. All these analyses were experienced in the Erzurum Virtual Tour case study, which was prepared to promote cultural heritage with the gamification method.

Implementation of Brain Computer Interface (BCI) as a Smart Wheelchair Motion Commands

A wheelchair is a tool used to assist people with physical limitations in their legs. The most widely used are standard wheelchairs with a manual operating system by being pushed by hand. However, people with disabilities who have paralysis or suffer from neuromuscular and neurological conditions cannot use this wheelchair. Because of this, in this study focuses on implementing the Brain Computer Interface system to generate five commands to move a wheelchair. There are five important stages in the BCI system, that is signal acquisition, pre-processing, feature extraction, classification, and applications interface. Fast Fourier Transform (FFT) method used to extract brainwave features. The results of FFT are alpha (8-12Hz) and beta (12-30 Hz) waves in the frequency domain. For classifying brain waves into six classes as input commands to drive a DC motor used Support Vector Machine (SVM) method. Based on the test results, the average accuracy of the classification for the whole class reached 93,1%, the accuracy of class 0 (77,3%), class 1 (95,7%), class 2 (97,8%), class 3 (98,0%), and class 4 (97,5%).

An efficient 3D column-only P300 speller paradigm utilizing few numbers of electrodes and flashings for practical BCI implementation.

The event related P300 potentials, positive waveforms in electroencephalography (EEG) signals, are often utilized in brain computer interfaces (BCI). Many studies have been carried out to improve the performance of P300 speller systems either by developing signal processing algorithms and classifiers with different architectures or by designing new paradigms. In this study, a new paradigm is proposed for this purpose. The proposed paradigm combines two remarkable properties of being a 3D animation and utilizing column-only flashings as opposed to classical paradigms which are based on row-column flashings in 2D manner. The new paradigm is utilized in a traditional two-layer artificial neural networks model with a single output neuron, and numerous experiments are conducted to evaluate and compare the performance of the proposed paradigm with that of the classical approach. The experimental results, including statistical significance tests, are presented for single and multiple EEG electrode usage combinations in 1, 3 and 15 flashing repetitions to detect P300 waves as well as to recognize target characters. Using the proposed paradigm, the best average classification accuracy rates on the test data are improved from 89.97% to 93.90% (an improvement of 4.36%) for 1 flashing, from 97.11% to 98.10% (an improvement of 1.01%) for 3 flashings and from 99.70% to 99.81% (an improvement of 0.11%) for 15 flashings when all electrodes, included in the study, are utilized. On the other hand, the accuracy rates are improved by 9.69% for 1 flashing, 4.72% for 3 flashings and 1.73% for 15 flashings when the proposed paradigm is utilized with a single EEG electrode (P8). It is observed that the proposed speller paradigm is especially useful in BCI systems designed for few EEG electrodes usage, and hence, it is more suitable for practical implementations. Moreover, all participants, given a subjective test, declared that the proposed paradigm is more user-friendly than classical ones.

A Calibration-free Approach to Implementing P300-based Brain–computer Interface

Introduction: As a direct bridge between the brain and the outer world, brain–computer interface (BCI) is expected to replace, restore, enhance, supplement, or improve the natural output of brain. The prospect of BCI serving humans is very broad. However, the extensive applications of BCI have not been fully achieved. One of reasons is that the cost of calibration reduces the convenience and usability of BCI. Methods: In this study, we proposed a calibration-free approach, which is based on the ideas of reinforcement learning and transfer learning, for P300-based BCI. This approach, composed of two algorithms: P300 linear upper confidence bound (PLUCB) and transferred PLUCB (TPLUCB), is able to learn during the usage by exploration and exploitation and allows P300-based BCI to start working without any calibration. Results: We tested the performances of PLUCB and TPLUCB using stepwise linear discriminant analysis (SWLDA), a commonly used method that needs calibration, as a baseline in simulated online experiments. The results showed the merits of PLUCB and TPLUCB. PLUCB can quickly increase the accuracies to the level of SWLDA. TPLUCB has surpassed SWLDA in the sample accuracy since it starts running. Both PLUCB and TPLUCB have the ability to keep improving the classification performance during the process. The overall sample accuracies (\(73.6\pm 4.8\%\), \(73.1\pm 4.9\%\)), overall symbol accuracies (\(80.4\pm 12.8\%\), \(79.6\pm 14.0\%\)), F-measures (\(0.45\pm 0.06\), \(0.44\pm 0.06\)) and information transfer ratios (ITR) (\(36.4\pm 9.1\), \(35.5\pm 9.8\)) of PLUCB and TPLUCB are significantly better than those of SWLDA (overall sample accuracy: \(58.8\pm 3.8\%\), overall symbol accuracy: \(69.0\pm 18.3\%\), F-measure: \(0.38\pm 0.04\), ITR: \(28.7\pm 10.7\)). Conclusions: The proposed approach, which does not need calibration but outperform SWLDA, is a very good option for the implementation of P300-based BCI.

Development of Software for Control of Virtual Objects Using Event-Related Potential P300

The article considers the principle of operation of brain-computer interfaces (BCI) and a method for detecting the focus of a person's attention using event-related potential (P300). The review of the existing hardware and software solutions for the implementation of BCIs was performed including the identification of their advantages and disadvantages. The program that allows you to choose the desired stimulus from a variety of presented was developed.An electroencephalograph of the BiTronics Lab company on the Arduino platform was used to receive the signal. Signal filtering, classifier training and visualization are implemented in Python.

Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks.

The steady-state visual evoked potential (SSVEP), which is a kind of event-related potential in electroencephalograms (EEGs), has been applied to brain–computer interfaces (BCIs). SSVEP-based BCIs currently perform the best in terms of information transfer rate (ITR) among various BCI implementation methods. Canonical component analysis (CCA) or spectrum estimation, such as the Fourier transform, and their extensions have been used to extract features of SSVEPs. However, these signal extraction methods have a limitation in the available stimulation frequency; thus, the number of commands is limited. In this paper, we propose a complex valued convolutional neural network (CVCNN) to overcome the limitation of SSVEP-based BCIs. The experimental results demonstrate that the proposed method overcomes the limitation of the stimulation frequency, and it outperforms conventional SSVEP feature extraction methods.

Brain–computer interface in the context of information retrieval systems in a library

PurposeThe subject of this paper is the idea of Brain–Computer Interface (BCI). The main goal is to assess the potential impact of BCI on the design, use and evaluation of information retrieval systems operating in libraries.Design/methodology/approachThe method of literature review was used to establish the state of research. The search according to accepted queries was carried out in the Scopus database and complementary in Google Scholar. To determine the state of research on BCI on the basis of library and information science, a specialist LISTA abstract database was also searched. The most current papers published in the years 2015–2019 in the English language or having at least an abstract in this language were taken into account.FindingsThe analysis showed that BCI issues are extremely popular in subject literature from various fields, mainly computer science, but practically does not occur in the context of using this technology in information retrieval systems.Research limitations/implicationsDue to the fact that BCI solutions are not yet implemented in libraries and are rarely the subject of scientific considerations in the field of library and information science, this article is mainly based on literature from other disciplines. The goal was to consider how much BCI solutions can affect library information retrieval systems. The considerations presented in this article are theoretical in nature due to the lack of empirical materials on which to base. The author's assumption was to initiate a discussion about BCI on the basis of library and information science, not to propose final solutions.Practical implicationsThe results can be widely used in practice as a framework for the implementation of BCI in libraries.Social implicationsThe article can help to facilitate the debate on the role of implementing new technologies in libraries.Originality/valueThe problem of BCI is very rarely addressed in the subject literature in the field of library and information science.

An EEG channel selection method for motor imagery based brain–computer interface and neurofeedback using Granger causality

Motor imagery (MI) brain–computer interface (BCI) and neurofeedback (NF) with electroencephalogram (EEG) signals are commonly used for motor function improvement in healthy subjects and to restore neurological functions in stroke patients. Generally, in order to decrease noisy and redundant information in unrelated EEG channels, channel selection methods are used which provide feasible BCI and NF implementations with better performances. Our assumption is that there are causal interactions between the channels of EEG signal in MI tasks that are repeated in different trials of a BCI and NF experiment. Therefore, a novel method for EEG channel selection is proposed which is based on Granger causality (GC) analysis. Additionally, the machine-learning approach is used to cluster independent component analysis (ICA) components of the EEG signal into artifact and normal EEG clusters. After channel selection, using the common spatial pattern (CSP) and regularized CSP (RCSP), features are extracted and with the k-nearest neighbor (k-NN), support vector machine (SVM) and linear discriminant analysis (LDA) classifiers, MI tasks are classified into left and right hand MI. The goal of this study is to achieve a method resulting in lower EEG channels with higher classification performance in MI-based BCI and NF by causal constraint. The proposed method based on GC, with only eight selected channels, results in 93.03% accuracy, 92.93% sensitivity, and 93.12% specificity, with RCSP feature extractor and best classifier for each subject, after being applied on Physionet MI dataset, which is increased by 3.95%, 3.73%, and 4.13%, in comparison with correlation-based channel selection method.

Operate P300 speller when performing other task

Objective. The P300 speller is a classic brain–computer interface (BCI) paradigm that has the potential to restore impaired motor control function. However, previous studies have confirmed that the letter recognition accuracy (LRA) of the P300 speller is a challenge when performing other tasks. Approach. To address this, we implemented a dynamic stopping strategy (DSS) to maintain the P300 speller LRA when performing multiple tasks simultaneously. Multiple tasks with dynamic workload levels were adopted to simulate the brain’s other thinking activities while operating P300 speller. A Bayes-based DSS offline model was built in single-task (only P300 speller task) and an online P300 speller system was established to test the DSS algorithm feasibility in dual-task. Main results. Online experimental results showed that the P300 speller with DSS could achieve a high LRA (96.9%) under dual-task, which was similar to single-task (98.7%, p = 0.126). Under dual-task, DSS dynamically adjusted the discriminant confidence according to the workload levels of the distraction tasks (correlation coefficient r = −0.68). Therefore, DSS can increase the repeated sequences to compensate for the reduction of P300 speller signal-to-noise ratio caused by parallel thinking activities. The average of repeated sequences increased significantly from 4.98 times under single-task to 6.22 times under dual-task (p < 0.005). These results indicated that the P300 speller feature is robust and the DSS model built in single-task maintained the applicability in various dual-tasks. Significance. Overall, this study provides a basis for the implementation of laboratory-developed BCI in real-world environments.

Sensorimotor ECoG Signal Features for BCI Control: A Comparison Between People With Locked-In Syndrome and Able-Bodied Controls.

The sensorimotor cortex is a frequently targeted brain area for the development of Brain-Computer Interfaces (BCIs) for communication in people with severe paralysis and communication problems (locked-in syndrome; LIS). It is widely acknowledged that this area displays an increase in high-frequency band (HFB) power and a decrease in the power of the low frequency band (LFB) during movement of, for example, the hand. Upon termination of hand movement, activity in the LFB band typically shows a short increase (rebound). The ability to modulate the neural signal in the sensorimotor cortex by imagining or attempting to move is crucial for the implementation of sensorimotor BCI in people who are unable to execute movements. This may not always be self-evident, since the most common causes of LIS, amyotrophic lateral sclerosis (ALS) and brain stem stroke, are associated with significant damage to the brain, potentially affecting the generation of baseline neural activity in the sensorimotor cortex and the modulation thereof by imagined or attempted hand movement. In the Utrecht NeuroProsthesis (UNP) study, a participant with LIS caused by ALS and a participant with LIS due to brain stem stroke were implanted with a fully implantable BCI, including subdural electrocorticography (ECoG) electrodes over the sensorimotor area, with the purpose of achieving ECoG-BCI-based communication. We noted differences between these participants in the spectral power changes generated by attempted movement of the hand. To better understand the nature and origin of these differences, we compared the baseline spectral features and task-induced modulation of the neural signal of the LIS participants, with those of a group of able-bodied people with epilepsy who received a subchronic implant with ECoG electrodes for diagnostic purposes. Our data show that baseline LFB oscillatory components and changes generated in the LFB power of the sensorimotor cortex by (attempted) hand movement differ between participants, despite consistent HFB responses in this area. We conclude that the etiology of LIS may have significant effects on the LFB spectral components in the sensorimotor cortex, which is relevant for the development of communication-BCIs for this population.

A Novel Features Selection Approach with Common Spatial Pattern for EEG Based Brain–Computer Interface Implementation

This paper presents a novel method for the selection of spatial filters and features in electroencephalography (EEG) based motor imagery classification. The analyzing EEG data are divided into training and test sets. The training set is used to select appropriate spatial filters with dominant features. To accomplish such features, the EEG of training set is segmented again into two subsets termed as training subset and test subset. The features of both subsets are extracted using common spatial pattern. Then features of training subset are ranked using mutual information based approach. Besides, the features of test subset are also ranked according to the order of the training subset features. The initial classification performance using training and test subsets are obtained by using linear discriminant analysis. Then a grid search method is employed to select the effective number of spatial filter pairs as well as the discriminative features. Thus obtained spatial filter and features are used in actual classification accuracy of the test set of EEG. The experimental results show that the proposed approach yields comparatively superior classification performance compared to prevailing methods.

A BCI Gaze Sensing Method Using Low Jitter Code Modulated VEP.

Visual evoked potentials (VEPs) are used in clinical applications in ophthalmology, neurology, and extensively in brain–computer interface (BCI) research. Many BCI implementations utilize steady-state VEP (SSVEP) and/or code modulated VEP (c-VEP) as inputs, in tandem with sophisticated methods to improve information transfer rates (ITR). There is a gap in knowledge regarding the adaptation dynamics and physiological generation mechanisms of the VEP response, and the relation of these factors with BCI performance. A simple, dual pattern display setup was used to evoke VEPs and to test signatures elicited by non-isochronic, non-singular, low jitter stimuli at the rates of 10, 32, 50, and 70 reversals per second (rps). Non-isochronic, low-jitter stimulation elicits quasi-steady-state VEPs (QSS-VEPs) that are utilized for the simultaneous generation of transient VEP and QSS-VEP. QSS-VEP is a special case of c-VEPs, and it is assumed that it shares similar generators of the SSVEPs. Eight subjects were recorded, and the performance of the overall system was analyzed using receiver operating characteristic (ROC) curves, accuracy plots, and ITRs. In summary, QSS-VEPs performed better than transient VEPs (TR-VEP). It was found that in general, 32 rps stimulation had the highest ROC area, accuracy, and ITRs. Moreover, QSS-VEPs were found to lead to higher accuracy by template matching compared to SSVEPs at 32 rps. To investigate the reasons behind this, adaptation dynamics of transient VEPs and QSS-VEPs at all four rates were analyzed and speculated.

Brain-Computer Interface (BCI) Applications in Mapping of Epileptic Brain Networks Based on Intracranial-EEG: An Update.

The main applications of the Brain–Computer Interface (BCI) have been in the domain of rehabilitation, control of prosthetics, and in neuro-feedback. Only a few clinical applications presently exist for the management of drug-resistant epilepsy. Epilepsy surgery can be a life-changing procedure in the subset of millions of patients who are medically intractable. Recording of seizures and localization of the Seizure Onset Zone (SOZ) in the subgroup of “surgical” patients, who require intracranial-EEG (icEEG) evaluations, remain to date the best available surrogate marker of the epileptogenic tissue. icEEG presents certain risks and challenges making it a frontier that will benefit from optimization. Despite the presentation of several novel biomarkers for the localization of epileptic brain regions (HFOs-spikes vs. Spikes for instance), integration of most in practices is not at the prime time as it requires a degree of knowledge about signal and computation. The clinical care remains inspired by the original practices of recording the seizures and expert visual analysis of rhythms at onset. It is becoming increasingly evident, however, that there is more to infer from the large amount of EEG data sampled at rates in the order of less than 1 ms and collected over several days of invasive EEG recordings than commonly done in practice. This opens the door for interesting areas at the intersection of neuroscience, computation, engineering and clinical care. Brain–Computer interface (BCI) has the potential of enabling the processing of a large amount of data in a short period of time and providing insights that are not possible otherwise by human expert readers. Our practices suggest that implementation of BCI and Real-Time processing of EEG data is possible and suitable for most standard clinical applications, in fact, often the performance is comparable to a highly qualified human readers with the advantage of producing the results in real-time reliably and tirelessly. This is of utmost importance in specific environments such as in the operating room (OR) among other applications. In this review, we will present the readers with potential targets for BCI in caring for patients with surgical epilepsy.

Four-Class Motor Imagery EEG Signal Classification using PCA, Wavelet and Two-Stage Neural Network

Electroencephalogram (EEG) is the most significant signal for brain-computer interfaces (BCI). Nowadays, motor imagery (MI) movement based BCI is highly accepted method for. This paper proposes a novel method based on the combined utilization of principal component analysis (PCA), wavelet packet transformation (WPT), and two-stage machine learning algorithm to classify four-class MI EEG signal. This work includes four-class MI events by an imaginary lifting of the left hand, right hand, left foot, and Right Foot. The main challenge of this work is to discriminate the similar lobe EEG signal pattern such as left foot VS left hand. Another critical problem is to identify the MI movements of two different feet because their activation level is very low and show an almost similar pattern. This work firstly uses the PCA to reduce the signal dimensions of the left and right lobe of the brain. Then, WPT is used to extract the feature from the different class EEG signal. Finally, the artificial neural network is trained into two stages – 1st stage identifies the lobe from the signal pattern and the 2nd stage identifies whether the signal is of MI hand or MI foot movement. The proposed method is applied to the 4-class MI movement related EEG signals of 15 participants and found excellent classification accuracy (>74% on average). The outcomes of the proposed method prove its effectiveness in practical BCI implementation.