Spectral EEG features and tasks selection process: Some considerations toward BCI applications

Brain-computer interfaces (BCIs) are at the forefront of medical technology, with transformative potential for neurological rehabilitation [1]. Motor imagery-based braincomputer interfaces (MI-BCIs) hold significant clinical value in neurorehabilitation [2,3]. By facilitating direct communication between the brain and external devices, BCIs are broadening the scope of neurorehabilitation, particularly for individuals with conditions such as spinal cord injury (SCI), stroke, and unilateral spatial neglect (USN) [4][5][6]. This special topic aims to highlight recent advancements, diverse applications, and the complex challenges associated with BCIs in managing neurological disorders. It includes four notable contributions, each providing unique insights into the potential of BCIs to enhance quality of life and functional abilities for those affected.In the study by Guo et al. on spinal cord injury (SCI), the authors explored a novel approach combining epidural electrical stimulation (EES) with near-infrared nerve stimulation (nINS) to enhance motor function specificity in SCI rats. The results revealed that adding optical stimulation to conventional EES could selectively activate target muscles with reduced stimulation intensity, offering an effective strategy to mitigate unwanted muscle activation and improve movement control. This dualstimulation approach presents a promising new avenue for addressing motor dysfunctions associated with SCI, paving the way for more precise neuromodulation in rehabilitation settings [7].patients. Using graph theory, the study assessed cortical reorganization in individuals undergoing VR-assisted therapy. The results demonstrated that the VR-based rehabilitation tool, Gesture Collection, significantly enhanced functional connectivity within motor-related brain regions compared to standard therapy. The improvement in connectivity, particularly within the frontoparietal and somatosensory networks, highlights the potential of VR to promote neuroplasticity and facilitate motor recovery in stroke survivors. By providing engaging and targeted motor exercises, VR-assisted rehabilitation could serve as a valuable complement to traditional stroke recovery programs [8].Wang et al. investigated the neural characteristics associated with neuropathic pain and numbness in SCI patients. Using electroencephalography (EEG), the study identified distinct brain network patterns differentiating patients with neuropathic pain from those experiencing numbness. Notably, individuals suffering from pain exhibited reduced power in lower frequency bands (θ and α) and increased power in the higher frequency band (β), accompanied by altered network connectivity. These findings suggest that EEG-based metrics could serve as valuable biomarkers for distinguishing different neuropathic symptoms in SCI patients, thereby informing personalized intervention strategies to improve chronic pain management in this population [9].The article by Gouret et al. provides a focused review of brain-computer interface (BCI) applications in the context of unilateral spatial neglect (USN), a common yet often underrecognized consequence of stroke. The review emphasizes the limited use of BCIs in addressing cognitive deficits, particularly visuo-attentional impairments, and underscores the need for expanded research in this area. The authors propose integrating VR with BCIs as a rehabilitation tool for USN, leveraging the cognitive engagement provided by VR to support attentional recovery. This perspective advocates for broadening the scope of BCI applications beyond motor rehabilitation to include cognitive therapy, thereby addressing a significant gap in current neurological treatment approaches [10] Collectively, these studies highlight the versatility and potential of brain-computer interfaces (BCIs) in neurological rehabilitation. By enhancing motor recovery, managing pain, and supporting cognitive restoration, BCIs represent a multidimensional tool capable of addressing various aspects of neurological impairments [11]. However, several challenges remain, including the need for improved user accessibility, device usability, and ethical considerations. As BCI technology continues to advance, interdisciplinary collaboration will be crucial for translating experimental insights into real-world solutions that can be widely adopted in clinical practice. For instance, integrating optogenetic BCIs with functional imaging techniques could allow targeted modulation of specific circuits, further promoting neurorehabilitation [12][13].This special issue aims to inspire ongoing research and innovation in the field of BCIs, with the ultimate goal of empowering individuals with neurological disorders to lead more independent and fulfilling lives. Through continued exploration and application, BCIs hold the promise of bridging the gap between neurological impairment and functional recovery, offering hope to patients, clinicians, and researchers alike.

The introduction of various neuroimaging methods, opened the doors to the field of brain–computer interfaces (BCI). Suddenly, controlling the elements of the world with your brain seemed to be possible, and that is what happened, given a few decades. The field of BCI does not simply aim to create a futuristic world, but also help people with neurodegenerative disorders. EEG-based BCIs (EEG-BCI) are the most common of all the BCI out there today. EEG-BCI consists of the hardware to record data, the experimental paradigm, and the signal processing pipeline. In this review, we will discuss a specific part of the processing pipeline, i.e., the classification techniques. More specifically the advancements, complexity, accuracy, and sensitivity in classification technique. We will also discuss the issues with the current trends of EEG-BCI.

Physical principles of brain–computer interfaces and their applications for rehabilitation, robotics and control of human brain states

BackgroundTime-Frequency analysis of electroencephalogram (EEG) during different mental tasks received significant attention. As EEG is non-stationary, time-frequency analysis is essential to analyze brain states during different mental tasks. Further, the time-frequency information of EEG signal can be used as a feature for classification in brain-computer interface (BCI) applications.MethodsTo accurately model the EEG, band-limited multiple Fourier linear combiner (BMFLC), a linear combination of truncated multiple Fourier series models is employed. A state-space model for BMFLC in combination with Kalman filter/smoother is developed to obtain accurate adaptive estimation. By virtue of construction, BMFLC with Kalman filter/smoother provides accurate time-frequency decomposition of the bandlimited signal.ResultsThe proposed method is computationally fast and is suitable for real-time BCI applications. To evaluate the proposed algorithm, a comparison with short-time Fourier transform (STFT) and continuous wavelet transform (CWT) for both synthesized and real EEG data is performed in this paper. The proposed method is applied to BCI Competition data IV for ERD detection in comparison with existing methods.ConclusionsResults show that the proposed algorithm can provide optimal time-frequency resolution as compared to STFT and CWT. For ERD detection, BMFLC-KF outperforms STFT and BMFLC-KS in real-time applicability with low computational requirement.

Brain machine interfaces (BMIs) provide one of the important means to interact with external world for a patient who lost motor function by using brain electrical signals such as scalp electroencephalogram (EEG). The main function of BMIs is to detect, analyze and classify the modulation of EEG according to a user's intent and to decode it to demonstrate to others. Most of conventional BMIs with relatively high speed and high classification accuracy have a requisite that a user can open and move his/her eyes toward a target corresponding to user's intent and gaze at it. Locked-in patients, who have lost almost all motor function including facial muscle, cannot control the eye-movement as well as gaze. The present study aimed at developing a BMI which was independent of the eye-movement and eye-gaze as a final goal. In this paper, we discuss the feasibility of binary class of BMI available with eyes-closed condition using a recent our newly observation that the steady-state visual evoked potential (SSVEP) could be modulated by performing the mental tasks even in the eyes-close state. Temporal evolution of the modulation of SSVEP was investigated using the amplitude of SSVEP and Kullback-Leibler (KL) divergence. The amplitudes of fundamental SSVEP (10 Hz) averaged across 11 participants showed significant difference between the mental focusing task and relaxed runs from the onset of the flicker stimuli with the frequency of 10 Hz and the intensity of 5 lx, whereas those of the second harmonic SSVEP (20 Hz) gave only small difference without statistical significance. For the image recalling task, the amplitude differences between the fundamental SSVEPs in the task and relaxed conditions did not show significant difference, whereas the amplitude differences of the second harmonic SSVEP showed the main effect of the image recall task. The KL divergences for the fundamental SSVEP amplitude showed large differences at the onset of flicker stimuli and gradual decrease with time at P4 and O2 for the mental focus task, yet no such characteristic evolution was not seen in KL divergence for the second harmonic SSVEP. On the other hand, the KL divergences during the image recalling were increased with time for both the fundamental and second harmonic SSVEPs at most of the electrode sites. The classification accuracies of the proposed BMI exceeded 75 % in average across the subjects for both tasks using Fisher's linear discriminant analysis.

Computer-based learning has gained popularity in recent years, providing learners greater flexibility and freedom. However, these learning environments do not consider the learner’s mental state in real-time, resulting in less optimized learning experiences. This research aimed to explore the effect on the learning experience of a novel EEG-based Brain-Computer Interface (BCI) that adjusts the speed of information presentation in real-time during a learning task according to the learner’s cognitive load. We also explored how motivation moderated these effects. In accordance with three experimental groups (non-adaptive, adaptive, and adaptive with motivation), participants performed a calibration task (n-back), followed by a memory-based learning task concerning astrological constellations. Learning gains were assessed based on performance on the learning task. Self-perceived mental workload, cognitive absorption and satisfaction were assessed using a post-test questionnaire. Between-group analyses using Mann–Whitney tests suggested that combining BCI and motivational factors led to more significant learning gains and an improved learning experience. No significant difference existed between the BCI without motivational factor and regular non-adaptive interface for overall learning gains, self-perceived mental workload, and cognitive absorption. However, participants who undertook the experiment with an imposed learning pace reported higher overall satisfaction with their learning experience and a higher level of temporal stress. Our findings suggest BCI’s potential applicability and feasibility in improving memorization-based learning experiences. Further work should seek to optimize the BCI adaptive index and explore generalizability to other learning contexts.

One of the key components of brain–computer interface (BCI) design is the interfacing paradigm. Progress in BCI research and applications significantly depends on a successful paradigm implementation. BCI paradigm requires simple interpretation and ease of use for end users so that electroencephalogram (EEG) data can be mapped to an application, for example, to drive a prosthetic arm or to send a command to operate an external device. Another remarkable use of the BCI system is to set up a communication network with exterior environment. Such actions necessitate the user to disburse voluntary or involuntary mental or control task without making the subject fatigued or tired. To ensure these criteria, the paradigm design should be leveraged to deliver optimal performance. P300 and SSVEP are two paradigms that have gathered large attentions from BCI communities. Over the last decade, advancement of BCI research has experienced successful design of various hybrid BCI systems. This chapter focuses on the issues and challenges involved during the development of BCI paradigm to harness the advantages associated with recently developed BCI paradigms. In addition, importance of paradigms will be highlighted to open up novel BCI applications for future use, which, in turn, might have a great potential to increase the BCI users to a large extent.

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

Brain-computer interface (BCI) as a rehabilitation tool has been used in restoring motor functions in patients with moderate to sever stroke impairments. To achieve the best possible outcome in such an application, it is highly desirable to have a stable and accurate operation of BCI. However, since electroencephalogram (EEG) signals considerably vary between sessions of even the same user, typically a long calibration session is recorded at the beginning of each session. This process is time-consuming and inconvenient for stroke patients who undergo long-term BCI sessions with repeating same mental tasks. This paper investigates the possibility of omitting the intra-session calibration for BCI-based stroke rehabilitation when large data recorded from the same user are available. For this purpose, a large dataset of EEG signals from 11 stroke patients performing 12 BCI-based stroke rehabilitation sessions over one month is used. Our offline results suggest that after recording a number of stroke rehabilitation sessions, the patient does not require calibration any more. The experimental results show that combining 11 sessions, which each session comprises minimum 60 trials per class, yields a model that averagely outperforms the standard calibration model trained by the data recorded directly before the test session.

Electroencephalography (EEG) is a non-invasive technique that records brain electrical activity, providing critical insights into neural processes. In recent years, EEG has become integral to brain-computer interface (BCI) research. BCIs enhance human-computer interaction, support assistive solutions for people with disabilities, and enable novel clinical applications. Research in EEG-based BCIs involves several key components: signal acquisition, preprocessing, feature extraction, and classification. Advanced machine learning models, especially those that emphasize personalized and incremental learning approaches, are used to effectively decode EEG signals. This personalization accounts for individual variability and significantly improves model accuracy and robustness. Applications of EEG-based BCIs include emotion recognition, motor imagery for robot control, and EEG-to-text decoding. These applications use EEG signals to make significant advances in their respective fields. Emotion recognition improves human-computer interaction and mental health monitoring; motor imagery enables intuitive robotic control that assists individuals with motor impairments; and EEG-to-text decoding provides new communication pathways for people with severe disabilities. Despite promising advances, challenges such as signal variability, noise, and the need for sophisticated preprocessing techniques remain. Future research should prioritize interdisciplinary collaboration and technological advancements to overcome these challenges, thereby enabling EEG-based BCIs to achieve broader applicability and significantly impact various aspects of human life.

Brain–computer interfaces (BCIs), also known as brain–machine interfaces (BMIs), translate brain activity into new outputs that replace, restore, enhance, supplement or improve natural brain outputs. BCI research and development has grown rapidly for the past two decades. It is beginning to provide useful communication and control capacities to people with severe neuromuscular disabilities; and it is expanding into new areas such as neurorehabilitation that may greatly increase its clinical impact. At the same time, significant challenges remain, particularly in regard to translating laboratory advances into clinical use.

Investigation of deep convolutional neural network for classification of motor imagery fNIRS signals for BCI applications

Subdural electrocorticography (ECoG) can provide a robust control signal for a brain-computer interface (BCI). However, the long-term recording properties of ECoG are poorly understood as most ECoG studies in the BCI field have only used signals recorded for less than 28 days. We assessed human ECoG recordings over durations of many months to investigate changes to recording quality that occur with long-term implantation. We examined changes in signal properties over time from 15 ambulatory humans who had continuous subdural ECoG monitoring for 184-766 days. Individual electrodes demonstrated varying changes in frequency power characteristics over time within individual patients and between patients. Group level analyses demonstrated that there were only small changes in effective signal bandwidth and spectral band power across months. High-gamma signals could be recorded throughout the study, though there was a decline in signal power for some electrodes. ECoG-based BCI systems can robustly record high-frequency activity over multiple years, albeit with marked intersubject variability. Group level results demonstrated that ECoG is a promising modality for long-term BCI and neural prosthesis applications.

Chapter 10 On the Use of Brain–Computer Interfaces Outside Scientific Laboratories: Toward an Application in Domotic Environments

Steady State Visual Evoked Potential (SSVEP) has been commonly adopted in Brain Computer Interface (BCI) applications. For wearable BCI applications, several aspects of SSVEP-based BCI systems, such as speed, subject variability, and accurate target detection, are under ongoing research investigations. Up to date, Canonical Correlation Analysis (CCA) has been considered the state-of-the-art feature extraction method for SSVEP-based BCI systems. Nevertheless, although CCA outperforms traditional SSVEP detection methods, such as Power Spectral Density Analysis (PSDA), achieving high accuracies when detecting target frequencies is still a challenging task due to user variation and physiological changes in the human body. In this paper, we investigate an SSVEP-based BCI application using wireless EEG recording and an Android tablet-based user interface. We propose a fusion of CCA and PSDA solutions at the score level by dividing their score space into multiple partitions, and extract and combine their complementary discriminative information to minimize the detection error in a linear fashion. We investigated transforming the fusion score space to lower dimensions with the purpose of alleviating redundancy. As such, we employed Principal Component Analysis (PCA), and Linear Discriminant Analysis (LDA). Our experimental results demonstrated that our proposed score fusion method is effective in reducing the effect of noise and non-stationary elements in EEG dynamics. Average detection accuracies improved from 63% for CCA to 72% for fusion+PCA and further improved to 98% for fusion+LDA.