Brain Signals Research Articles

Using Brain Waves and Computer Interface Technology as a Communication System

ABSTRACT Introduction The existing methods for individual emergency alert systems often rely on physical or voice-based human intervention, which may not be practical or safe in certain emergency situations or for people with certain rare medical conditions or disabilities. Popular voice command programs such as Siri and Alexa can be loud, drawing unwanted attention. Additionally, existing devices are limited to indoor usage, lack portability, involve multiple wires, have low noise tolerance, and offer limited customization options. This study introduces a novel method for emergency alert using brain waves. Method An electroencephalography (EEG) headset device was used to capture the user’s brain waves. After calibration, the device identifies peak brain signals and stores them for future use. When a command is triggered, the device’s Bluetooth functionality communicates with a dedicated application installed on any digital device. The user can use their thoughts to select a predefined command within the application, which is then transmitted to any local WiFi network or internet connection. Results Overall, this pilot study achieved a success rate of 96–98% for receiving the brain-computer interface (BCI) commands and sending the appropriate SMS text messages. Conclusion By leveraging these technologies, disabled individuals may access and use new technologies, starting with the ability to text message using their mind.

Controlling Virtual Reality With Brain Signals: State of the Art of Using VR-Based Feedback in Neurofeedback Applications.

The rapid progress of commercial virtual reality (VR) technology, open access to VR development software as well as open-source instructions for creating brain-VR interfaces have increased the number of VR-based neurofeedback (NF) training studies. Controlling a VR environment with brain signals has potential advantages for NF applications. More entertaining, multimodal and adaptive virtual feedback modalities might positively affect subjective user experience and could consequently enhance NF training performance and outcome. Nevertheless, there are certain pitfalls and contraindications that make VR-based NF not suitable for everyone. In the present review, we summarize applications of VR-based NF and discuss positive effects of VR-based NF training as well as contraindications such as cybersickness in VR or age- and sex-related differences. The existing literature implies that VR-based feedback is a promising tool for the improvement of NF training performance. Users generally rate VR-based feedback more positively than traditional 2D feedback, albeit to draw meaningful conclusions and to rule out adverse effects of VR, more research on this topic is necessary. The pace in the development of brain-VR synchronization furthermore necessitates ethical considerations on these technologies.

The different impacts of functional network centrality and connectivity on the complexity of brain signals in healthy control and first-episode drug-naïve patients with major depressive disorder.

In recent years, brain signal complexity has gained attention as an indicator of brain well-being and a predictor of disease and dysfunction. Brain entropy quantifies this complexity. Assessment of functional network centrality and connectivity reveals that information communication induces neural signal oscillations in certain brain regions. However, their relationship is uncertain. This work studied brain signal complexity, network centrality, and connectivity in both healthy and depressed individuals. The current work comprised a sample of 124 first-episode drug-naïve patients with major depressive disorder (MDD) and 105 healthy controls (HC). Six functional networks were created for each person using resting-state functional magnetic resonance imaging. For each network, entropy, centrality, and connectivity were computed. Using structural equation modeling, this study examined the associations between brain network entropy, centrality, and connectivity. The findings demonstrated substantial correlations of entropy with both centrality and connectivity in HC and these correlation patterns were disrupted in MDD. Compared to HC, MDD exhibited higher entropy in four networks and demonstrated changes in centralities across all networks. The structural equation modeling showed that network centralities, connectivity, and depression severity had impacts on brain entropy. Nevertheless, no impacts were observed in the opposite directions. This study indicated that the complexity of brain signals was influenced not only by the interactions among different areas of the brain but also by the severity level of depression. These findings enhanced our comprehension of the associations of brain entropy with its influential factors.

Abstract Su805: Early-Latency Evoked Brain Response Revealed using Novel Filtering Technique

Background: Cardiac arrest (CA) can cause coma in resuscitated patients. Accuracy of prognosis is limited. Presence of brain signals such as somatosensory evoked potential (SSEP) reveals integrity of the thalamocortical pathway, which is necessary for arousal but insufficient to predict positive outcome. The N10 is a SSEP component that diminishes in CA. The N7, which can be obscured by stimulus artifact or other components, is associated with subcortical structures. Phase space area (PSA) quantifies SSEP morphology and is associated with CA recovery. It has been measured at least 5ms after stimulus. Hypothesis: Early SSEP and PSA diminish under anesthesia and during CA but may recover afterwards. Aims: By removing stimulus artifact, we capture early SSEP (N7 and early PSA) to enable improved prognostication. Methods: Six rats received 1.2, 1.8, and 2.5% isoflurane anesthesia for 30min. Two underwent 5 or 7min CA and were observed over 2hr. SSEP was recorded every 2s. An adaptive filter removed the artifact. Peaks were measured near 7 (N7) and 15 (N10) ms post-stimulus. PSA, the area occupied by the phase space curve, was recorded over 0-9ms (“early”) and 0-30ms (“total”). Signal-to-noise ratio (SNR) is the ratio of N10 amplitude to standard deviation of the signal. T tests were used for comparisons between SNR values and between experimental conditions. Results: We identified 4153 N7 peaks with latency (mean±95% C.I.) 8.6±1.3ms in the filtered data, compared to 2464 with latency 8.5±1.4ms in the unfiltered data. SNR was greater in the filtered signal (2.3±1.5) than unfiltered (0.3±0.4). N7 amplitude decreased at the highest anesthesia level; N10 decreased for each level. N7 and N10 decreased during CA and recovered after, although N10 remained below baseline during 2hr recovery. Early PSA was lower under the higher anesthesia levels, while total PSA decreased for each level. PSA decreased during CA and recovered after 5min CA; total PSA recovered to baseline and early PSA exceeded it after 2hr.Early PSA did not recover from 7min CA, while total PSA recovered but remained below baseline. Conclusion: We demonstrate a novel technique to discover an elusive early-latency response, adding detail to a neural pathway involved in arousal. We reduce noise, capture N7 consistently, and measure PSA beginning at 0ms, providing additional diagnostic dimensions responsive to anesthesia and CA. Future study will investigate outcomes in resuscitated patients.

Clinical efficacy of low-dose Perampanel correlates with neurophysiological changes in familial adult myoclonus epilepsy 2.

Familial adult myoclonus epilepsy (FAME) management relies on antiseizure medications (ASMs), which inadequately address myoclonus and cortical tremor. This study evaluates Perampanel (PER), an AMPA-receptor antagonist, for treating FAME symptoms. Fifteen FAME2 patients participated in an observational prospective study. They received up to 6 mg daily of PER and underwent Unified-Myoclonus-Rating-Scale (UMRS) before and after treatment. Neurophysiological evaluations, including somatosensory evoked potentials (SEPs) and transcranial magnetic stimulation (TMS), assessed PER's impact on cortical glutamatergic excitatory and GABAergic inhibitory circuits. PER treatment significantly reduced UMRS total scores (p = 0.001) and action-myoclonus subscores (p = 0.002), irrespective of disease duration, age at onset, or testing time (p >0.05). Patients with more severe baseline myoclonus demonstrated significant improvements. Neurophysiological assessments revealed a PER-induced decrease in sensorimotor hyperexcitability, characterized by diminished N33 amplitudes, attenuated glutamatergic facilitation, and enhanced GABAergic inhibition in the motor cortex. In conclusion, low-dose PER is well tolerated and effective in alleviating myoclonus in FAME2 patients, supported by its modulatory effects on glutamatergic and GABAergic neuronal circuits. Plain Language Summary: This study investigated the effects of low-dose perampanel in individuals with Familial Adult Myoclonus Epilepsy2 (FAME2), a hereditary condition characterized by epilepsy and tremors. Perampanel, an antiepileptic drug, blocks AMPA receptors in the brain, reducing excessive neural activity that causes seizures and abnormal movements. The results showed significant symptom improvement, which correlated with changes in brain activity as measured by neurophysiological tests. This study suggests that perampanel helps regulate abnormal brain signals and may help managing FAME2 symptoms.

A Novel Filter Bank and Fourier Transform Convolutional Neural Network for SSVEP Classification

Abstract. Classifying Steady-State Visual Evoked Potentials (SSVEPs) is crucial for enhancing the performance of Brain-Computer Interface (BCI) systems. This study introduces a new method for SSVEP classification within BCI frameworks, called the Filter Bank and Fourier Transform Convolutional Neural Network (FBFCNN). The FBFCNN model effectively extracts key harmonic features by first segmenting SSVEP signals into multiple sub-bands through a filter bank. Subsequently, the Fast Fourier Transform (FFT) is applied to convert these signals from the time domain into the frequency domain. The generated spectrum's real and imaginary components are then fed into a convolutional neural network (CNN). This method improves SSVEP feature extraction and increases classification accuracy by fusing the advantages of CNNs with filter banks. Experimental results, derived from publicly available SSVEP datasets, show that the FBFCNN model surpasses traditional techniques in both accuracy and Information Transfer Rate (ITR). Offering a reliable and efficient solution for real-time brain signal decoding, the FBFCNN model represents a significant advancement in SSVEP-based BCI systems.

BRAIN COMPUTER INTERFACE: THE IMPACT ON NEUROPLASTICITY AND MOTOR LEARNING-A NARRATIVE REVIEW.

Background: Neurological disorders, especially stroke, have now become the major causes of death all around the world. They are also the leading cause of impairments and disabilities among those who survive which leads to dependency in ADL’s and an impairment in overall quality of life. Even though traditional rehabilitation techniques and advances in technology (VR/AR/GAMING/ASSISTIVE DEVICES/EXOSKELETONS) play a major role in the recovery process, not all individuals show a 100% positive outcome. Research suggests that the combination of BCI with traditional rehab techniques or advanced technologies help in increasing neuroplasticity by translating the brain signals into commands that can control external devices thereby leading to improved functions and activities. Objective: The objective of this review is to summarize the available evidence and to highlight the mechanism through which the BCI’s promote neuroplasticity and enhance motor functions. Methods: Articles were searched and reviewed from PubMed, research gate, google scholar using the terms BCI, neuroplasticity, motor functions, neurorehabilitation etc. only the studies which were fully accessible; showed positive outcomes; focusing on motor recovery and neuro rehab were included and the studies which focused on other uses of BCI such as gaming, commercial applications and those that focused on measuring non motor functions and cognitive impairments were excluded. Results and Conclusion: After reviewing the articles it was concluded that BCI in combination with conventional rehab techniques and advanced technologies promote neuroplasticity thereby improving functions through repeated activation of neurons and improving coordination and motor function by enhancing the brain connectivity. Implications: The invention of BCI and its combination with other rehab processes aids in the improvement of motor recovery and an overall improvement in ADL’s and quality of life of patients suffering from various neurological disorders.

Characterizing EEG signal dynamics in healthy, seizure-free, and seizure states using the chaos decision tree algorithm

Abstract Epilepsy is a multifaceted neurological condition marked by repetitive seizures that arise from irregular electrical activity in the brain. To understand this condition, a thorough examination of brain signals captured in different states is needed. In order to examine the dynamic behavior of brain signals in three different conditions: healthy, seizure-free, and seizure periods, this study uses the chaos decision tree algorithm. The findings show notable variations in these situations’ dynamics. Chaos is evident during seizure moments, showing extremely chaotic activity. The signals mostly exhibit stochastic behavior in the healthy condition, which is consistent with typical brain dynamics. It is noteworthy that an intermediate state exhibiting a blend of stochastic and chaotic signal dynamics is exhibited throughout the seizure-free time. Furthermore, the research shows that the frequency of chaotic signals rises with increasing proximity to the epileptogenic zone. These discoveries clarify the complex nature of epilepsy and offer insightful information about the dynamic properties of brain signals in various stages, aiding in improved understanding and potential diagnostic approaches.

Machine Learning-based Filtering system for fNIRS Signals’ Analysis Purpose

This paper presents a preliminary study delving into the application of machine learning-based methods for optimising parameter selection in filtering techniques. The authors focus on exploring the efficacy of two prominent filtering methods: smoothing and cascade filters, known for their profound impact on enhancing the quality of brain signals. The study specifically examines signals acquired through functional near-infrared spectroscopy (fNIRS), a non-invasive neuroimaging modality offering valuable insights into brain activity. Through meticulous analysis, the research underscores the potential of machine learning approaches in discerning optimal parameters for filtering, thereby leading to a significant enhancement in the quality and reliability of fNIRS-derived signals. The results demonstrate the effectiveness of machine learning-based methods in optimizing parameter selection for filtering techniques, particularly in the context of fNIRS signals. By leveraging these approaches, the study achieves notable improvements in the quality and reliability of brain signal data. This work sheds light on promising avenues for refining neuroimaging methodologies and advancing the field of signal processing in neuroscience. The successful application of machine learning-based techniques highlights their potential for optimizing neuroimaging data processing, ultimately contributing to a deeper understanding of brain function.

Resting state of human brain measured by fMRI experiment is governed more dominantly by essential mode as a global signal rather than default mode network

Resting-state of the human brain has been described by a combination of various basis modes including the default mode network (DMN) identified by fMRI BOLD signals in human brains. Whether DMN is the most dominant representation of the resting-state has been under question. Here, we investigated the unexplored yet fundamental nature of the resting-state. In the absence of global signal regression for the analysis of brain-wide spatial activity pattern, the fMRI BOLD spatiotemporal signals during the rest were completely decomposed into time-invariant spatial-expression basis modes (SEBMs) and their time-evolution basis modes (TEBMs). Contrary to our conventional concept above, similarity clustering analysis of the SEBMs from 166 human brains revealed that the most dominant SEBM cluster is an asymmetric mode where the distribution of the sign of the components is skewed in one direction, for which we call essential mode (EM), whereas the second dominant SEBM cluster resembles the spatial pattern of DMN. Having removed the strong 1/f noise in the power spectrum of TEBMs, the genuine oscillatory behavior embedded in TEBMs of EM and DMN-like mode was uncovered around the low-frequency range below 0.2 Hz.

Endogenous Attention Affects Decision-related Neural Activity but Not Afferent Visual Responses.

Endogenous shifts of spatial attention toward an upcoming stimulus are associated with improvements in behavioral responses to the stimulus, preparatory retinotopic shifts in alpha power, and changes in ERPs. Although attentional modulation of several early sensory ERPs is well established, there is still debate about under what circumstances attention affects the earliest cortical visual evoked response-the C1 ERP component-which is putatively generated from afferent input into primary visual cortex. Moreover, the effects of spatial attention on the recently discovered ERP signature of evidence accumulation-the central parietal positivity (CPP)-have not been fully characterized. The present study assessed the effect of spatial attention on the C1 and CPP components through a spatially cued contrast discrimination task using stimuli that were specifically designed to produce large-amplitude C1 responses and that varied in sensory evidence strength to characterize the CPP. Participants responded according to which of two checkerboard stimuli had greater contrast following an 80% valid cue toward the upper or lower visual field. Prestimulus alpha power changed topographically based on the cue, suggesting participants shifted attention to prepare for the upcoming stimuli. Despite these attentional shifts in alpha power and the fact that the stimuli reliably elicited C1 responses several times greater than many prior studies, there was no evidence of an attention effect on the C1. The CPP, however, showed a clear increase in build-up rate on valid trials. Our findings suggest that endogenous attention may not affect the early C1 ERP component but may improve behavior at a decision stage, as reflected in brain signals related to evidence accumulation (the CPP).

Calcineurin/NFAT inhibitors maintain cognition in a preclinical prevention study in an aging canine model of Alzheimer disease

Brain signaling of calcineurin (CN) and nuclear factor of activated T-cells (NFAT) transcription factor increases in Alzheimer disease (AD) and is associated with synaptic loss, neurodegeneration, neuroinflammation, amyloid-β (Aβ) production, and cognitive decline. CN/NFAT inhibitors ameliorate these neuropathologies in mouse models of AD. Further, chronic use of tacrolimus in transplant patients reduces risk of AD. Beagles naturally develop Aβ plaques and cognitive dysfunction. We evaluated the impact of FDA-approved CN inhibitor, tacrolimus, and experimental NFAT inhibitor, Q134R, on cognitive outcomes during a three-year prevention study in 37 middle-aged beagles. While beagles treated with CN/NFAT inhibitors showed differences in the pattern of cognitive maintenance and duration of their effect, there was improvement in spatial learning, as well as maintenance of memory, attention, and working memory relative to placebo dogs. CN/NFAT inhibition is a promising target for prevention of cognitive decline that may be rapidly implemented in human clinical trials.

Comparison Between Methods of Choosing EEG Training Data in Epileptic Seizures Prediction Systems

Considering the large number of people that suffer with epilepsy, there is great interest in developing a system capable of predicting the occurrence of the seizures. Warning patients about the proximity of a seizure can help them to avoid dangerous situations. Many efforts in the development of this system have been placed using, basically, machine learning techniques. In most of these works, regardless of the technique employed, it is assumed that the system is able to learn when the patient’s brain signals, obtained from electroencephalogram (EEG), change from interictal to pre-ictal state. Some published works do not explicitly clarify the method of choosing training and test data. This means that good results may be due to the use of an inadequate method. That is, the EEG segments may have been temporarily mixed up during training, which would help the network correctly predict the seizure. However, this would not work from a real point of view. The objective of this work is to investigate, through experiments, the effect of the way of choosing the training and test data on the accuracy of a seizure prediction system. Experimental results showed that the accuracy of the systems increases when training is performed with windows close to the seizure used for testing.

Decoding Brain Signals from Rapid-Event EEG for Visual Analysis Using Deep Learning.

The perception and recognition of objects around us empower environmental interaction. Harnessing the brain's signals to achieve this objective has consistently posed difficulties. Researchers are exploring whether the poor accuracy in this field is a result of the design of the temporal stimulation (block versus rapid event) or the inherent complexity of electroencephalogram (EEG) signals. Decoding perceptive signal responses in subjects has become increasingly complex due to high noise levels and the complex nature of brain activities. EEG signals have high temporal resolution and are non-stationary signals, i.e., their mean and variance vary overtime. This study aims to develop a deep learning model for the decoding of subjects' responses to rapid-event visual stimuli and highlights the major factors that contribute to low accuracy in the EEG visual classification task.The proposed multi-class, multi-channel model integrates feature fusion to handle complex, non-stationary signals. This model is applied to the largest publicly available EEG dataset for visual classification consisting of 40 object classes, with 1000 images in each class. Contemporary state-of-the-art studies in this area investigating a large number of object classes have achieved a maximum accuracy of 17.6%. In contrast, our approach, which integrates Multi-Class, Multi-Channel Feature Fusion (MCCFF), achieves a classification accuracy of 33.17% for 40 classes. These results demonstrate the potential of EEG signals in advancing EEG visual classification and offering potential for future applications in visual machine models.

A Relationship of Monoamine Oxidase-A with Serotonin in Violent Antisocial Behavior in Iraqi Prisoners

It is thought that the monoamine oxidase-A (MAOA) enzyme, which catalyzes key brain signaling chemicals like serotonin, noradrenaline, and dopamine, indirectly influences antisocial behavior by disrupting the neurotransmitter balance in brain regions and neural networks. This study aims to assess the MAOA enzyme level in serum and serotonin linked to antisocial behavior in cases in the prison of Baghdad City, Iraq. Blood samples were collected from (63) prisoners and (27) control groups, ages 18 years to 65 years, with a mean ± SD of 37.8 ± 8.8, all males, with different prison terms and different crimes for prisoners in the prison at Baghdad City, Iraq. MAOA and serotonin levels were measured in serum in prisoners and healthy men, and the results showed the serum levels of MAOA enzyme and serotonin were decreased in prisoners compared to the healthy (11.5 IU/ml vs. 19.5 IU/ml) and (79 IU/ml vs. 439 IU/ml), respectively, with the observed difference being significant (P < 0.001(. The results of the relationship between education and social violence were: illiterate 11.1%, primary school 27%, secondary 52.4%, and university 9.5%. The median serum level of MAOA and serotonin in the age group 18–39 years was (11.3 IU/ml, 68.3 ng/ml), respectively, and in the age group 40–65 years was (13 IU/ml, 139 ng/ml), respectively.

Ginsenoside Rg1 enriches gut microbial indole-3-acetic acid to alleviate depression-like behavior in mice via oxytocin signaling

Background and purposeAlthough a large collection of data has shown that ginsenosides, the major active ingredients from Ginseng, have neuroprotective and anti-depressant effect, the mechanism of action is incompletely understood. This study aims to elucidate the antidepressant mechanism of ginsenoside Rg1 (Rg1), a poorly absorbed ginsenoside, from the perspective of gut microbe to brain signaling. MethodsA mouse model of depression was induced by unpredictable mild stress (UMS). Behavioral and neurochemical tests were conducted to evaluate the effect and mechanism of Rg1 on depressive behavior. Non-target and target metabolomics were performed to identify the signaling metabolites underlying the antidepressant efficacy of Rg1. Gut microbial structure was analyzed by 16S rRNA sequencing and the potential functional strains associated with Rg1 action were investigated by in vitro bacterial culture. Chemical intervention was used to explore the mechanism of Rg1 and signaling metabolite. ResultsRg1 improved UMS-induced despair, anxiety-like and social avoidance behaviors in mice, which were accompanied by increased hypothalamic oxytocin secretion and restored neural proliferation in the hippocampus. Metabolomic analysis of the gut-brain axis revealed that Rg1 increased the concentration of serum and brain indole-3-acetic acid (IAA), a bacterial metabolite that was partially attributed to the enrichment of Lactobacillus murinus in the gut microbiome. Oral supplementation of IAA mimicked the anti-depressant action of Rg1, while oxytocin receptor antagonist abrogated the anti-depressant effects of both Rg1 and IAA. ConclusionOur work provides a new gut-to-brain signaling mechanism for the antidepressant effects of Rg1. In particular, Rg1 enriches the abundance of Lactobacillus murinus, which in turn increases the level of brain IAA and potentiates hypothalamic oxytocin signal. These findings suggest a promising pathway for producing antidepressant effects through gut-brain crosstalk.

Preclinical and first-in-human evaluation of AL002, a novel TREM2 agonistic antibody for Alzheimer’s disease

BackgroundVariants of the gene triggering receptor expressed on myeloid cells-2 (TREM2) increase the risk of Alzheimer’s disease (AD) and other neurodegenerative disorders. Signaling by TREM2, an innate immune receptor expressed by microglia, is thought to enhance phagocytosis of amyloid beta (Aβ) and other damaged proteins, promote microglial proliferation, migration, and survival, and regulate inflammatory signaling. Thus, TREM2 activation has potential to alter the progression of AD. AL002 is an investigational, engineered, humanized monoclonal immunoglobulin G1 (IgG1) antibody designed to target TREM2. In AD mouse models, an AL002 murine variant has been previously shown to induce microglial proliferation and reduce filamentous Aβ plaques and neurite dystrophy.MethodsPreclinical studies assessed the safety, tolerability, pharmacokinetics, and pharmacodynamics of AL002 in cynomolgus monkeys. INVOKE-1 (NCT03635047) was a first-in-human phase 1, randomized, placebo-controlled, double-blind study assessing the safety, tolerability, PK, and PD of AL002 administered as single ascending doses (SAD) in healthy volunteers.ResultsIn cynomolgus monkeys, weekly intravenous injections of AL002 for 4 weeks were well tolerated, dose-dependently decreased soluble TREM2 (sTREM2) in cerebrospinal fluid (CSF) and total TREM2 in hippocampus and frontal cortex, and increased biomarkers of TREM2 signaling in CSF and brain. In the phase 1 study of 64 healthy volunteers, a single intravenous infusion of AL002 demonstrated brain target engagement based on a dose-dependent reduction of sTREM2 in CSF and parallel increases in biomarkers of TREM2 signaling and microglia recruitment. Single-dose AL002 showed central nervous system penetrance and was well tolerated, with no treatment-related serious adverse events over 12 weeks.ConclusionsThese findings support the continued clinical development of AL002 for AD and other neurodegenerative diseases in which TREM2 activation may be beneficial. AL002 is currently being tested in a phase 2, randomized, double-blind, placebo-controlled study in early AD.Trial registrationClinicaltrials.gov, NCT03635047. Registered on August 15, 2018, https://www.clinicaltrials.gov/study/NCT03635047.

Thought-Controlled Computer Applications: A Brain–Computer Interface System for Severe Disability Support

This study introduces an integrated computational environment that leverages Brain–Computer Interface (BCI) technology to enhance information access for individuals with severe disabilities. Traditional assistive technologies often rely on physical interactions, which can be challenging for this demographic. Our innovation focuses on creating new assistive technologies that use novel Human–Computer interfaces to provide a more intuitive and accessible experience. The proposed system offers four key applications to users controlled by four thoughts: an email client, a web browser, an e-learning tool, and both command-line and graphical user interfaces for managing computer resources. The BCI framework translates ElectroEncephaloGraphy (EEG) signals into commands or events using advanced signal processing and machine learning techniques. These identified commands are then processed by an integrative strategy that triggers the appropriate actions and provides real-time feedback on the screen. Our study shows that our framework achieved an 82% average classification accuracy using four distinct thoughts of 62 subjects and a 95% recognition rate for P300 signals from two users, highlighting its effectiveness in translating brain signals into actionable commands. Unlike most existing prototypes that rely on visual stimulation, our system is controlled by thought, inducing brain activity to manage the system’s Application Programming Interfaces (APIs). It switches to P300 mode for a virtual keyboard and text input. The proposed BCI system significantly improves the ability of individuals with severe disabilities to interact with various applications and manage computer resources. Our approach demonstrates superior performance in terms of classification accuracy and signal recognition compared to existing methods.

Machine Learning Techniques Towards Accurate Emotion Classification from EEG Signals

This article delves into using machine learning algorithms for emotion classification via EEG brain signals. The goal is to discover an accurate model beyond traditional methods, necessitating AI for classifying emotional EEG signals. This study, motivated by the complex link between emotions and neural activity, employs Random Forest, Support Vector Machines, and K-Nearest Neighbors. Notably, Random Forest achieves 99% accuracy, SVM 98%, and KNN 94%. These impressive results, backed by performance metrics like confusion matrices, reveal each model’s effectiveness in emotion classification. The dataset, rich in varied emotional stimuli and EEG placements, provides a robust foundation for detailed analysis. This research underscores significant applications in affective computing and mental health, offering a promising path to understanding the intricate relationship between EEG signals and human emotions.

A Lightweight GCT-EEGNet for EEG-Based Individual Recognition Under Diverse Brain Conditions

A robust biometric system is essential to mitigate various security threats. Electroencephalography (EEG) brain signals present a promising alternative to other biometric traits due to their sensitivity, non-duplicability, resistance to theft, and individual-specific dynamics. However, existing EEG-based biometric systems employ deep neural networks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which face challenges such as high parameter complexity, limiting their practical application. Additionally, their ability to generalize across a large number of subjects remains unclear. Moreover, they have been validated on datasets collected in controlled environments, which do not accurately reflect real-world scenarios involving diverse brain conditions. To overcome these challenges, we propose a lightweight neural network model, GCT–EEGNet, which is based on the design ideas of a CNN model and incorporates an attention mechanism to pay attention to the appropriate frequency bands for extracting discriminative features relevant to the identity of a subject despite diverse brain conditions. First, a raw EEG signal is decomposed into frequency bands and then passed to GCT–EEGNet for feature extraction, which utilizes a gated channel transformation (GCT) layer to selectively emphasize informative features from the relevant frequency bands. The extracted features were used for subject recognition through a cosine similarity metric that measured the similarity between feature vectors of different EEG trials to identify individuals. The proposed method was evaluated on a large dataset comprising 263 subjects. The experimental results demonstrated that the method achieved a correct recognition rate (CRR) of 99.23% and an equal error rate (EER) of 0.0014, corroborating its robustness against different brain conditions. The proposed model maintains low parameter complexity while keeping the expressiveness of representations, even with unseen subjects.