Based Brain-computer Interface Research Articles

Attention-based CNN model for motor imagery classification from nonlinear EEG signals

Motor imagery (MI)-based brain-computer interface (BCI) provides a promising solution for the limb rehabilitation of stroke patients. For better rehabilitation performance, high-precision classification of MI-related EEG signals plays a critical role. However, this is still a challenging problem for multi-category MI signals. In this paper, we focus on four commonly used stroke rehabilitation actions, and propose a modular temporal-spatial attention-based CNN (MTSACNN) model for MI classification. In detail, we carry out the MI experiments and acquire the EEG signals related to imagining left/right fist clenching and left/right wrist dorsiflexion. MTSACNN model firstly extracts the low-order MI features through the temporal-spatial feature extraction module (TSFE module). Especially, a group attention mechanism is proposed for intra-group information interaction. Secondly, considering the short- and long-term working characteristics of brain, high-order temporal features are further extracted and fused by the multi-level feature fusion module (MLFF module). Finally, four auxiliary losses are arranged in the classification module (C module) to speed up the model optimization process. The experimental results show that MTSACNN model can achieve good performance in decoding rehabilitation-related MI brain intentions, achieving an average classification accuracy of 72.05% for fourteen subjects. This work is beneficial to promote the construction of high-performance stroke rehabilitation BCI system.

A temporal–spectral fusion transformer with subject-specific adapter for enhancing RSVP-BCI decoding

The Rapid Serial Visual Presentation (RSVP)-based Brain–Computer Interface (BCI) is an efficient technology for target retrieval using electroencephalography (EEG) signals. The performance improvement of traditional decoding methods relies on a substantial amount of training data from new test subjects, which increases preparation time for BCI systems. Several studies introduce data from existing subjects to reduce the dependence of performance improvement on data from new subjects, but their optimization strategy based on adversarial learning with extensive data increases training time during the preparation procedure. Moreover, most previous methods only focus on the single-view information of EEG signals, but ignore the information from other views which may further improve performance. To enhance decoding performance while reducing preparation time, we propose a Temporal-Spectral fusion transformer with Subject-specific Adapter (TSformer-SA). Specifically, a cross-view interaction module is proposed to facilitate information transfer and extract common representations across two-view features extracted from EEG temporal signals and spectrogram images. Then, an attention-based fusion module fuses the features of two views to obtain comprehensive discriminative features for classification. Furthermore, a multi-view consistency loss is proposed to maximize the feature similarity between two views of the same EEG signal. Finally, we propose a subject-specific adapter to rapidly transfer the knowledge of the model trained on data from existing subjects to decode data from new subjects. Experimental results show that TSformer-SA significantly outperforms comparison methods and achieves outstanding performance with limited training data from new subjects. This facilitates efficient decoding and rapid deployment of BCI systems in practical use.

A brief survey on human activity recognition using motor imagery of EEG signals.

Human being's biological processes and psychological activities are jointly connected to the brain. So, the examination of human activity is more significant for the well-being of humans. There are various models for brain activity detection considering neuroimaging for attaining decreased time requirement, increased control commands, and enhanced accuracy. Motor Imagery (MI)-based Brain-Computer Interface (BCI) systems create a way in which the brain can interact with the environment by processing Electroencephalogram (EEG) signals. Human Activity Recognition (HAR) deals with identifying the physiological activities of human beings based on sensory signals. This survey reviews the different methods available for HAR based on MI-EEG signals. A total of 50 research articles based on HAR from EEG signals are considered in this survey. This survey discusses the challenges faced by various techniques for HAR. Moreover, the papers are assessed considering various parameters, techniques, publication year, performance metrics, utilized tools, employed databases, etc. There were many techniques developed to solve the problem of HAR and they are classified as Machine Learning (ML) and Deep Learning (DL)models. At last, the research gaps and limitations of the techniques were discussed that contribute to developing an effective HAR.

User-wise perturbations for user identity protection in EEG-based BCIs.

An electroencephalogram (EEG)-based brain-computer interface (BCI) is a direct communication pathway between the human brain and a computer. Most research so far studied more accurate BCIs, but much less attention has been paid to the ethics of BCIs. Aside from task-specific information, EEG signals also contain rich private information, e.g., user identity, emotion, disorders, etc., which should be protected. We show for the first time that adding user-wise perturbations can make identity information in EEG unlearnable. We propose four types of user-wise privacy-preserving perturbations, i.e., random noise, synthetic noise, error minimization noise, and error maximization noise. After adding the proposed perturbations to EEG training data, the user identity information in the data becomes unlearnable, while the BCI task information remains unaffected. Experiments on six EEG datasets using three neural network classifiers and various traditional machine learning models demonstrated the robustness and practicability of the proposed perturbations. Our research shows the feasibility of hiding user identity information in EEG data without impacting the primary BCI task information.

Towards imagined speech: Identification of brain states from EEG signals for BCI-based communication systems

BackgroundThe electroencephalogram (EEG) based brain-computer interface (BCI) system employing imagined speech serves as a mechanism for decoding EEG signals to facilitate control over external devices or communication with the external world at the moment the user desires. To effectively deploy such BCIs, it is imperative to accurately discern various brain states from continuous EEG signals when users initiate word imagination. New methodThis study involved the acquisition of EEG signals from 15 subjects engaged in four states: resting, listening, imagined speech, and actual speech, each involving a predefined set of 10 words. The EEG signals underwent preprocessing, segmentation, spatio-temporal and spectral analysis of each state, and functional connectivity analysis using the phase locking value (PLV) method. Subsequently, five features were extracted from the frequency and time-frequency domains. Classification tasks were performed using four machine learning algorithms in both pair-wise and multiclass scenarios, considering subject-dependent and subject-independent data. ResultsIn the subject-dependent scenario, the random forest (RF) classifier achieved a maximum accuracy of 94.60 % for pairwise classification, while the artificial neural network (ANN) classifier achieved a maximum accuracy of 66.92 % for multiclass classification. In the subject-independent scenario, the random forest (RF) classifier achieved maximum accuracies of 81.02 % for pairwise classification and 55.58 % for multiclass classification. Moreover, EEG signals were classified based on frequency bands and brain lobes, revealing that the theta (θ) and delta (δ) bands, as well as the frontal and temporal lobes, are sufficient for distinguishing between brain states. ConclusionThe findings promise to develop a system capable of automatically segmenting imagined speech segments from continuous EEG signals.

Adversarial artifact detection in EEG-based brain–computer interfaces

Objective. machine learning has achieved significant success in electroencephalogram (EEG) based brain-computer interfaces (BCIs), with most existing research focusing on improving the decoding accuracy. However, recent studies have shown that EEG-based BCIs are vulnerable to adversarial attacks, where small perturbations added to the input can cause misclassification. Detecting adversarial examples is crucial for both understanding this phenomenon and developing effective defense strategies.Approach. this paper, for the first time, explores adversarial detection in EEG-based BCIs. We extend several popular adversarial detection approaches from computer vision to BCIs. Two new Mahalanobis distance based adversarial detection approaches, and three cosine distance based adversarial detection approaches, are also proposed, which showed promising performance in detecting three kinds of white-box attacks.Main results. we evaluated the performance of eight adversarial detection approaches on three EEG datasets, three neural networks, and four types of adversarial attacks. Our approach achieved an area under the curve score of up to 99.99% in detecting white-box attacks. Additionally, we assessed the transferability of different adversarial detectors to unknown attacks.Significance. through extensive experiments, we found that white-box attacks may be easily detected, and differences exist in the distributions of different types of adversarial examples. Our work should facilitate understanding the vulnerability of existing BCI models and developing more secure BCIs in the future.

The Development of a Multicommand Tactile Event-Related Potential-Based Brain–Computer Interface Utilizing a Low-Cost Wearable Vibrotactile Stimulator

A tactile event-related potential (ERP)-based brain–computer interface (BCI) system is an alternative for enhancing the control and communication abilities of quadriplegic patients with visual or auditory impairments. Hence, in this study, we proposed a tactile stimulus pattern using a vibrotactile stimulator for a multicommand BCI system. Additionally, we observed a tactile ERP response to the target from random vibrotactile stimuli placed in the left and right wrist and elbow positions to create commands. An experiment was conducted to explore the location of the proposed vibrotactile stimulus and to verify the multicommand tactile ERP-based BCI system. Using the proposed features and conventional classification methods, we examined the classification efficiency of the four commands created from the selected EEG channels. The results show that the proposed vibrotactile stimulation with 15 stimulus trials produced a prominent ERP response in the Pz channels. The average classification accuracy ranged from 61.9% to 79.8% over 15 stimulus trials, requiring 36 s per command in offline processing. The P300 response in the parietal area yielded the highest average classification accuracy. The proposed method can guide the development of a brain–computer interface system for physically disabled people with visual or auditory impairments to control assistive and rehabilitative devices.

A Systematic Investigation Based on BCI and EEG Implemented using Machine Learning Algorithms

BCI is a strong tool that improves human-system communication. It improves the brain's ability to interact with its surroundings. Recent decades have seen substantial advances in neuroscience and computer science. This has made BCI a leader in computational neuroscience and intelligence research. Recent technological advances including wearable sensing devices, real-time data streaming, machine learning, and deep learning have raised the need for electroencephalographic (EEG)-based brain-computer interface (BCI) in clinical and translational applications. EEG-based Brain-Computer Interfaces (BCIs) detect cognitive state variations throughout laborious tasks, making them advantageous for individuals. To fill in the gaps in the wide overview of the past five years (2019-2024), we surveyed the newest research on EEG signal detection and computational intelligence in brain-computer interfaces. To provide a more accurate account, we will begin by reviewing Brain-Computer Interface (BCI) technology and its main challenges. Modern signal detection and enhancement techniques for EEG signal collection and refinement follow. We also provide advanced computational intelligence methods for tracking, maintaining, and monitoring human cognitive and operational performance in everyday applications. Combinations, interpretable fuzzy models, transfer learning, and deep learning are used. We conclude with a sample of cutting-edge BCI-driven healthcare applications and explore future EEG-based BCI research.

Ensemble strategies exploration for the calibration data optimized spatial filters based SSVEP recognition algorithms

Steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) exhibit high information transfer rate (ITR) characteristics. However, the electroencephalogram (EEG) data encoding SSVEP patterns has a low signal-to-noise ratio, leading to limitations in applications. To enhance the application of SSVEP-BCIs, various SSVEP recognition algorithms have been developed by utilizing prior knowledge based on the calibration data optimized spatial filters (CDOSF). Yet, the process of combining spatial filters from multiple stimuli remains unexplored, resulting in performance bottlenecks for SSVEP recognition and application scenarios. In this study, we provide an overview of four popular CDOSF-based SSVEP recognition algorithms and introduce three ensemble strategies to extend these recognition algorithms. For comparison purposes, we selected three benchmark SSVEP datasets and conducted comparative experiments on the three ensemble strategies across the four recognition algorithms. The experimental results demonstrate that the ensemble strategies significantly enhance SSVEP recognition accuracy and ITR. Ablation studies also confirm that ensemble strategies possess characteristics of requiring less calibration data, fewer EEG channels, and insensitivity to parameters. The ensemble strategies extended CDOSF-based recognition algorithms offer a novel choice for constructing SSVEP-BCIs.

Deep learning in motor imagery EEG signal decoding: A Systematic Review

Thanks to the fast evolution of electroencephalography (EEG)-based brain–computer interfaces (BCIs) and computing technologies, as well as the availability of large EEG datasets, decoding motor imagery (MI) EEG signals is rapidly shifting from traditional machine learning (ML) to deep learning (DL) approaches. Furthermore, real-world MI-EEG BCI applications are progressively requiring higher generalization capabilities, which can be achieved by leveraging publicly available MI-EEG datasets and high-performance decoding models. Within this context, this paper provides a systematic review of DL approaches for MI-EEG decoding, focusing on studies that work on publicly available EEG-MI datasets. This review paper firstly provides a clear overview of these datasets that can be used for DL model training and testing. Afterwards, considering each dataset, related DL studies are discussed with respect to the four decoding paradigms identified in the literature, i.e., subject-dependent, subject-independent, transfer learning, and global decoding paradigms. Having analyzed the reviewed studies, the current trends and strategies, popular architectures, baseline models that are used for comprehensive analysis, and techniques to ensure reproducibility of the results in DL-based MI-EEG decoding are also identified and discussed. The selection and screening of the studies included in this review follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, leading to a comprehensive analysis of 394 papers published between January 1, 2017, and January 23, 2023.

Enhancing complex upper-limb motor imagery discrimination through an incremental training strategy

Motor Imagery (MI)-based Brain–Computer Interface (BCI) systems are a great technological advance for the recovery of lost movements in people with severe motor impairments. Different Artificial Intelligence (AI) techniques with supervised methods have been explored for MI task discrimination, especially static movements from left and right hands. Due to different factors affecting MI-based Electroencephalography (EEG) signals related to physical and cognitive conditions, the success rate of BCIs is still low. Currently, there is a need to explore the MI of complex movements associated with Activities of Daily Living (ADLs), which brings challenges to the scientific community. In this work, an incremental training methodology for Artificial Neural Networks (ANNs) is proposed for discrimination of complex MI tasks. MI-Rest and MI-Task related to the imagination of manipulating a drinking cup were discriminated by implementing an Action Observation (AO)-based protocol in a first-person 2D virtual reality. Thirty healthy individuals were recruited to evaluate our complex MI classification approach. The incremental training proposed achieves significantly higher performance (p<0.05) compared to the non-incremental training. Additionally, the proposed method is significantly superior compared to widely applied methods for MI task discrimination: Power Spectrum (PS), Common Spatial Patterns (CSP), Filter Bank Common Spatial Patterns (FBCSP), each using four baseline Machine Learning (ML) methods for classification. The results show an improvement between 5 and 20% according to both Accuracy (ACC) and False Positive Rate (FPR). The proposed methodology obtained promising results, useful for AI-based BCI training, which would allow the development of more robust systems for neurorehabilitation purposes.

High-frequency SSVEP-BCI system for detecting intermodulation frequency components using task-discriminant component analysis

Recently, steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has significantly progressed and is moving from the laboratory to practical application. However, the system performance and comfort of SSVEP-BCIs still need to be improved. In this study, five flicker frequencies (i.e., 30–34 Hz with an interval of 1 Hz) and eight scaling frequencies (i.e., 0.4–1.8 Hz with an interval of 0.2 Hz) were adopted to jointly encode forty visual stimulus targets using evoked intermodulation (IM) frequency components. Both luminance and shape changes are implemented by sinusoidal sampling stimulus coding methods. High-frequency flicker frequencies and green visual stimuli were chosen to improve the comfort of the proposed system. An extended version of a training algorithm named task-discriminant component analysis (TDCA) was proposed to detect the IM components of SSVEP signals. The average recognition accuracy of eleven subjects is 96.82 ± 0.01 % in the offline experiments for a data length of 5 s. Online validation experiments was constructed from the optimized parameters of offline analysis, and the average accuracy and ITR were 94.37 ± 1.17 % and 113.47 ± 2.60 bits/min, respectively. Furthermore, ten subjects who participated in the validation part also completed the online free-spell task successfully. These results showed that it is feasible to expand the number of stimulus targets by using IM frequency components of SSVEP signals for target coding, and that the system performance is superior.

Enhancing Domain Diversity of Transfer Learning-Based SSVEP-BCIs by the Reconstruction of Channel Correlation.

The application of transfer learning, specifically pre-training and fine-tuning, in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has been demonstrated to effectively improve the classification performance of deep learning methods with limited calibration data. However, effectively learning task-related knowledge from source domains during the pre-training phase remains challenging. To address this issue, this study proposes an effective data augmentation method called Reconstruction of Channel Correlation (RCC) to optimize the utilization of the source domain data. Concretely, RCC reconstructs training samples using probabilistically mixed eigenvector matrices derived from covariance matrices across source domains. This process manipulates the channel correlation of training samples, implicitly creating novel synthesized domains. By increasing the diversity of source domains, RCC aims to enhance the domain generalizability of the pre-trained model. The effectiveness of RCC is validated through subject-independent and subject-adaptive classification experiments. The results of subject-independent classification demonstrate that RCC significantly improves the classification performance of the pre-trained model on unseen target subjects. Moreover, when compared to the fine-tuning process using the RCC-absent pre-trained model, the fine-tuning process using the RCC-enhanced pre-trained model yields significantly improved performance in the subject-adaptive classification. RCC proves to enhance the performance of transfer learning by optimizing the utilization of the source domain data. The RCC-enhanced transfer learning has the potential to facilitate the practical implementation of SSVEP-BCIs in real-world scenarios.

Decoding working-memory load during n-back task performance from high channel fNIRS data

Objective.Functional near-infrared spectroscopy (fNIRS) can measure neural activity through blood oxygenation changes in the brain in a wearable form factor, enabling unique applications for research in and outside the lab and in practical occupational settings. fNIRS has proven capable of measuring cognitive states such as mental workload, often using machine learning (ML) based brain-computer interfaces (BCIs). To date, this research has largely relied on probes with channel counts from under ten to several hundred, although recently a new class of wearable NIRS devices featuring thousands of channels has emerged. This poses unique challenges for ML classification, as fNIRS is typically limited by few training trials which results in severely under-determined estimation problems. So far, it is not well understood how such high-resolution data is best leveraged in practical BCIs and whether state-of-the-art or better performance can be achieved.Approach.To address these questions, we propose an ML strategy to classify working-memory load that relies on spatio-temporal regularization and transfer learning from other subjects in a combination that, to our knowledge, has not been used in previous fNIRS BCIs. The approach can be interpreted as an end-to-end generalized linear model and allows for a high degree of interpretability using channel-level or cortical imaging approaches.Main results.We show that using the proposed methodology, it is possible to achieve state-of-the-art decoding performance with high-resolution fNIRS data. We also replicated several state-of-the-art approaches on our dataset of 43 participants wearing a 3198 dual-channel NIRS device while performing then-Back task and show that these existing methodologies struggle in the high-channel regime and are largely outperformed by the proposed pipeline.Significance.Our approach helps establish high-channel NIRS devices as a viable platform for state-of-the-art BCI and opens new applications using this class of headset while also enabling high-resolution model imaging and interpretation.

Distraction impact of concurrent conversation on event-related potential based brain-computer interfaces

Objective.This study investigates the impact of conversation on the performance of visual event-related potential (ERP)-based brain-computer interfaces (BCIs), considering distractions in real life environment. The research aims to understand how cognitive distractions from speaking and listening activities affect ERP-BCI performance.Approach.The experiment employs a dual-task paradigm where participants control a smart light using visual ERP-BCIs while simultaneously conducting speaking or listening tasks.Main results.The findings reveal that speaking notably degrades BCI accuracy and the amplitude of ERP components, while increases the latency variability of ERP components and occipital alpha power. In contrast, listening and simple syllable repetition tasks have a lesser impact on these variables. The results suggest that speaking activity significantly distracts visual attentional processes critical for BCI operationSignificance. This study highlights the need to take distractions by daily conversation into account of the design and implementation of ERP-BCIs.

Improving inter-session performance via relevant session-transfer for multi-session motor imagery classification

Motor imagery (MI)-based brain-computer interfaces (BCIs) using electroencephalography (EEG) have found practical applications in external device control. However, the non-stationary nature of EEG signals remains to obstruct BCI performance across multiple sessions, even for the same user. In this study, we aim to address the impact of non-stationarity, also known as inter-session variability, on multi-session MI classification performance by introducing a novel approach, the relevant session-transfer (RST) method. Leveraging the cosine similarity as a benchmark, the RST method transfers relevant EEG data from the previous session to the current one. The effectiveness of the proposed RST method was investigated through performance comparisons with the self-calibrating method, which uses only the data from the current session, and the whole-session transfer method, which utilizes data from all prior sessions. We validated the effectiveness of these methods using two datasets: a large MI public dataset (Shu Dataset) and our own dataset of gait-related MI, which includes both healthy participants and individuals with spinal cord injuries. Our experimental results revealed that the proposed RST method leads to a 2.29 % improvement (p < 0.001) in the Shu Dataset and up to a 6.37 % improvement in our dataset when compared to the self-calibrating method. Moreover, our method surpassed the performance of the recent highest-performing method that utilized the Shu Dataset, providing further support for the efficacy of the RST method in improving multi-session MI classification performance. Consequently, our findings confirm that the proposed RST method can improve classification performance across multiple sessions in practical MI-BCIs.

Nonlinear difference subspace method of motor imagery EEG classification in brain-computer interface

Noninvasive Motor Imagery (MI) electroencephalography (EEG) based brain-computer interface (BCI) systems are in high demand in both the medical and consumer sectors. However, the low signal-to-noise ratio of EEG and its nonlinear dynamics present challenges in processing. The effectiveness of information retrieval is directly related to the complexity of the algorithms, making BCI impractical for real-time applications. To enhance performance, this study introduced classification frameworks using nonlinear subspace and difference subspace methods, employing manifold learning techniques such as Kernel principal component analysis (KPCA) and Isometric feature mapping (Isomap). The optimal dimensions for the subspaces were selected using overlapping criteria in conjunction with the breadth-first-search (BFS) and depth-first-search (DFS) graph search methods. The proposed methods were evaluated on the BCI competition III and IV datasets using metrics including classification accuracy, Cohen's kappa coefficient, and F1-score. By exploiting the nonlinear characteristics of EEG and employing the geodesic distance between all pairs of trials, the modified Isomap called supervised discriminant Isomap projection (SD-ISoP) outperformed the KPCA-based methods and other recently proposed state-of-the-art methods. The results demonstrssate that the SD-ISoP-based difference subspace method achieved an average classification accuracy of 86.25% and 85.44% for BCI competition III and IV datasets, respectively.

Comparative Study of Frequency Recognition Techniques for Steady-State Visual Evoked Potentials According to the Frequency Harmonics and Stimulus Number.

A key challenge in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems is to effectively recognize frequencies within a short time window. To address this challenge, the specific characteristics of the data are needed to select the frequency recognition method. These characteristics include factors, such as the number of stimulation targets and the presence of harmonic frequencies, resulting in optimizing the performance and accuracy of SSVEP-based BCI systems. The current study aimed to examine the effect of data characteristics on frequency recognition accuracy. In this analytical study, five commonly used frequency recognition methods were examined, used to various datasets containing different numbers of frequencies, including sub-data with and without frequency harmonics. The increase in the number of frequencies in the Multivariate Linear Regression (MLR) method has led to a decrease in frequency recognition accuracy by 9%. Additionally, the presence of harmonic frequencies resulted in an 8% decrease in accuracy for the MLR method. Frequency recognition using the MLR method reduces the effect of the number of different frequencies and harmonics of the stimulation frequencies on the frequency recognition accuracy.

Filter bank temporally local multivariate synchronization index for SSVEP-based BCI

BackgroundMultivariate synchronization index (MSI) has been successfully applied for frequency detection in steady state visual evoked potential (SSVEP) based brain–computer interface (BCI) systems. However, the standard MSI algorithm and its variants cannot simultaneously take full advantage of the time-local structure and the harmonic components in SSVEP signals, which are both crucial for frequency detection performance. To overcome the limitation, we propose a novel filter bank temporally local MSI (FBTMSI) algorithm to further improve SSVEP frequency detection accuracy. The method explicitly utilizes the temporal information of signal for covariance matrix estimation and employs filter bank decomposition to exploits SSVEP-related harmonic components.ResultsWe employed the cross-validation strategy on the public Benchmark dataset to optimize the parameters and evaluate the performance of the FBTMSI algorithm. Experimental results show that FBTMSI outperforms the standard MSI, temporally local MSI (TMSI) and filter bank driven MSI (FBMSI) algorithms across multiple experimental settings. In the case of data length of one second, the average accuracy of FBTMSI is 9.85% and 3.15% higher than that of the FBMSI and the TMSI, respectively.ConclusionsThe promising results demonstrate the effectiveness of the FBTMSI algorithm for frequency recognition and show its potential in SSVEP-based BCI applications.

A Wearable Device-Based System: The Potential Role in Real-time and Remote EEG Monitoring

Purpose: In recent years, the Electroencephalography (EEG)-based Brain-Computer Interface (BCI) application has been growing rapidly and has emerged as a technology with high translational potential since it allows disabled people to translate human intentions into control signals and to interact with the external environment without any kinesthetic movement. Individuals with significant health problems can benefit from this technology to improve their independence and facilitate participation in activities, thus improving general well-being and preventing impairments. The rapid advances in technology have led to optimal innovation in the context of wearable health monitoring and remote control solutions. Many wearable devices for capturing EEG signals in daily life have recently been released on the market. Our paper aims to present a wearable de-vice-based system for real-time and remote EEG monitoring, to describe the proposed system modules and the signal processing algorithms, to explore the functionalities of this wearable EEG solution, and to suggest its potential application for daily brain data recordings in the home environment. Materials and Methods: The validity of the Emotiv Epoc+ device in the continuous and real-time EEG signals acquisition and monitoring was demonstrated by means of preliminary test measurements during a resting state paradigm performed by a healthy control subject. Results: Our findings confirmed the Epoc+ reliability for event-related brain potential research and in measuring acceptable spectral EEG data. Conclusion: The described approach, focusing on the emerging area of remote monitoring sensors and wearable device applications, might be leveraged to measure complex health outcomes in non-specialist and remote settings.