Progression of EEG-BCI Classification Techniques: A Study

In this paper, we further develop the idea of subject specific mental tasks selection process as a necessary prerequisite in any EEG-based brain computer interface (BCI) application. While, in two previous researches we proved — using the EEG-extracted auto-regressive (AR) parameters and twelve different mental tasks —, the major gains one can obtain in tasks classification performance only by selecting the proper tasks, here we investigate the putative relation that exists between each (subject, given EEG features) pair and the corresponding individual optimum set of cognitive tasks. In this idea, a set of three different spectrum relative power parameters were considered. The classification performances achieved with these last EEG features are comparatively presented for two subjects and for two sets of tasks: i) the frequently used in the BCI field, Keirn and Aunon set of tasks, and ii) the previously determined (AR-based) optimum individual set of tasks.

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.

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.

Brain-Computer Interface (BCI) systems allow user to operate devices without muscular activation and experimental results indicate that they can induce activity-dependent plasticity. A BCI system has two key features: exploitation of brain signal changes induced by the cognitive task assigned to the user and mutual learning. With respect to the first feature, BCI users can learn to control and hence change their cortical activity, for example by performing a motor imagery task. Mutual learning, on the other hand, is set up between users, which have to learn how to control their brain activity, and the classifier used for learning and categorize brain signal changes. This mutual learning can be mediated by the use of feedback, which in turn can be discrete or continuous, abstract or realistic and so on. Motor imagery represents a promising approach for stroke rehabilitation, thus a BCI based on a motor imagery paradigm could be a useful tool. Nevertheless, in stroke rehabilitation settings two issues are emerging which are also crucial for motor imagery BCIs. The first one relates to the importance of recognize the imagery ability of the subject, the second one to the importance of feedback in re-learning of motor skills. The present study relates to this second issue and it is aimed at investigating attitudes of BCI users towards feedback. We considered a sample of six healthy males, using an EEG-based BCI and executing a Motor Imagery task without feedback (training phase) and with feedback (performance phase). A horizontal bar of varying length depicted on a computer screen was used as feedback medium and overall accuracy data were matched with neurophysiological data acquired from P z and O z . Results could be of importance for BCI use as neurorehabilitation tool in stroke rehabilitation protocols since they suggest that some subjects could need an increased cortical activation in order to deal with a stimulus like visual feedback. Moreover, they reveal a possible association between worsening in performance and increased cortical activation needed in order to cope with feedback.

The demand for public datasets has increased as data-driven methodologies have been introduced in the field of brain-computer interfaces (BCIs). Indeed, many BCI datasets are available in various platforms or repositories on the web, and the studies that have employed these datasets appear to be increasing. Motor imagery is one of the significant control paradigms in the BCI field, and many datasets related to motor tasks are open to the public already. However, to the best of our knowledge, these studies have yet to investigate and evaluate the datasets, although data quality is essential for reliable results and the design of subject− or system-independent BCIs. In this study, we conducted a thorough investigation of motor imagery/execution EEG datasets recorded from healthy participants published over the past 13 years. The 25 datasets were collected from six repositories and subjected to a meta-analysis. In particular, we reviewed the specifications of the recording settings and experimental design, and evaluated the data quality measured by classification accuracy from standard algorithms such as Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) for comparison and compatibility across the datasets. As a result, we found that various stimulation types, such as text, figure, or arrow, were used to instruct subjects what to imagine and the length of each trial also differed, ranging from 2.5 to 29 s with a mean of 9.8 s. Typically, each trial consisted of multiple sections: pre-rest (2.38 s), imagination ready (1.64 s), imagination (4.26 s, ranging from 1 to 10 s), the post-rest (3.38 s). In a meta-analysis of the total of 861 sessions from all datasets, the mean classification accuracy of the two-class (left-hand vs. right-hand motor imagery) problem was 66.53%, and the population of the BCI poor performers, those who are unable to reach proficiency in using a BCI system, was 36.27% according to the estimated accuracy distribution. Further, we analyzed the CSP features and found that each dataset forms a cluster, and some datasets overlap in the feature space, indicating a greater similarity among them. Finally, we checked the minimal essential information (continuous signals, event type/latency, and channel information) that should be included in the datasets for convenient use, and found that only 71% of the datasets met those criteria. Our attempts to evaluate and compare the public datasets are timely, and these results will contribute to understanding the dataset’s quality and recording settings as well as the use of using public datasets for future work on BCIs.

A brain-computer interface (BCI) establishes a direct communication pathway between the brain and an external device. Electroencephalogram (EEG) is the most popular input signal in BCIs, due to its convenience and low cost. Most research on EEG-based BCIs focuses on the accurate decoding of EEG signals; however, EEG signals also contain rich private information, e.g., user identity, emotion, and so on, which should be protected. This paper first exposes a serious privacy problem in EEG-based BCIs, i.e., the user identity in EEG data can be easily learned so that different sessions of EEG data from the same user can be associated together to more reliably mine private information. To address this issue, we further propose two approaches to convert the original EEG data into identity-unlearnable EEG data, i.e., removing the user identity information while maintaining the good performance on the primary BCI task. Experiments on seven EEG datasets from five different BCI paradigms showed that on average the generated identity-unlearnable EEG data can reduce the user identification accuracy from 70.01% to at most 21.36%, greatly facilitating user privacy protection in EEG-based BCIs.

Due to large intra-subject and inter-subject variabilities of electroencephalogram (EEG) signals, EEG-based brain-computer interfaces (BCIs) usually need subject-specific calibration to tailor the decoding algorithm for each new subject, which is time-consuming and user-unfriendly, hindering their real-world applications. Transfer learning (TL) has been extensively used to expedite the calibration, by making use of EEG data from other subjects/sessions. An important consideration in TL for EEG-based BCIs is to reduce the data distribution discrepancies among different subjects/sessions, to avoid negative transfer. Euclidean alignment (EA) was proposed in 2020 to address this challenge. Numerous experiments from 13 different BCI paradigms demonstrated its effectiveness and efficiency. This paper revisits EA, explaining its procedure and correct usage, introducing its applications and extensions, and pointing out potential new research directions. It should be very helpful to BCI researchers, especially those who are working on EEG signal decoding.

Brain-Computer Interfaces (BCI), allowing to operate the enviroment without muscular activation, can be used as assistive technology as well as neurorehabilitation tool. A BCI exploits brain signal changes induced by event-related potentials (P300) or by voluntary control of the user (Slow Cortical Potentials or Motor Imagery) and this can be achieved using a classifier. A key feature for BCI systems is user-classifier mutual learning, which can be modulated by feedback. There is experimental evidence that feedback can have a positive as well as a negative effect and this is relevant in using BCI both as assistive technology and as neurorehabilitation tool. The aim of this study was to investigate a possible sequence effect on overall accuracy in administering a first training session without feedback and then a second one with feedback. Moreover, we evaluated also a gender effect. We considered a sample of sixteen subjects (ten males), using an EEG-based BCI and executing a Motor Imagery task. Our results, combined with data from literature, indicate the presence of a “cognitive carry-over” effect for continous feedback in motor imagery, with gender differences not statistically significant. Such a cognitive effect means that the presence or absence of feedback changes the cognitive status of the BCI user and thus the information embedded in the features the classifier uses. Therefore, there should be a sort of cognitive coherence in BCI-training and the assessment of user attitude toward feedback could improve BCI usefulness and effectiveness.

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.

As an important branch of man-machine interaction, Brain-Computer Interface (BCI) has the potential to be widely used in various fields, such as health-care, physical efficiency, aerospace, intelligence traffic system, entertainment and so on. The flexible electrode is the crucial component of the BCI, and is the key for the development of the BCI technology. Recently, with the increasing demands on Brain-Computer Interface, plenty of flexible electrode materials and the structural design for applications in BCI technology have been developed. In this study, we review the development of the three kinds of flexible electrodes material selection and structural design in BCI, including non-intrusive electrodes, intrusive electrodes and semi-intrusive electrodes. The challenge and the problems that hinder the development of the flexible electrode are analyzed. Besides, from the perspective of material science, the future applications of the flexible electrode in the BCI field is prospected.

EEG-based brain computer interface (BCI) provides a new communication channel between human brain and computer. The classification of EEG data is an important task in EEG-based BCI. In this paper we present a new modification on classic “Template Matching” to make a better detection for a specific pattern in EEG. The new method shows a significant improvement in P300 detection which is a common approach in BCI systems. The proposed model uses more than one scalp electrodes and combines the outputs with a fuzzy technique, to detect P300 cognitive component.

Thirty-eight chapters ago, we started this handbook with an introduction that began with brain-computer interface (BCI) technology trends and historical events that have broken ground for BCI research and development. We thought that the world is ready for a new handbook that comprehensively addresses the recent and rapid changes in the field of BCIs. However, this handbook was not intended to provide a single blueprint for BCIs. Rather, it highlighted a synopsis of key findings and technological and theoretical advances directly applicable to brain-computer interfacing technologies, readily understood and applied by individuals with no formal training in BCI research and development.

P.1.g.029 - Liraglutide in psychiatric patients: preliminary results

As the main component of Brain-computer interface (BCI) technology, the classification algorithm based on EEG has developed rapidly. The previous algorithms were often based on subject-dependent settings, resulting in BCI needing to be calibrated for new users. In this work, we propose IMH-Net, an end-to-end subject-independent model. The model first uses Inception blocks extracts the frequency domain features of the data, then further compresses the feature vectors to extract the spatial domain features, and finally learns the global information and classification through Multi-Head Attention mechanism. On the OpenBMI dataset, IMH-Net obtained 73.90 ± 13.10% accuracy and 73.09 ± 14.99% F1-score in subject-independent manner, which improved the accuracy by 1.96% compared with the comparison model. On the BCI competition IV dataset 2a, this model also achieved the highest accuracy and F1-score in subject-dependent manner. The IMH-Net model we proposed can improve the accuracy of subject-independent Motor Imagery (MI), and the robustness of the algorithm is high, which has strong practical value in the field of BCI.

DARPA-funded efforts in the development of novel brain–computer interface technologies