Children diagnosed with Attention-Deficit/Hyperactivity Disorder (ADHD) face many difficulties in maintaining their concentration (in terms of attention levels) and controlling their behaviors. Previous studies have mainly focused on identifying brain regions involved in cognitive processes or classifying ADHD and control subjects. However, the classification of attention levels of ADHD subjects has not yet been explored. Here, a robust Swin Transformer (Swin-T) model is proposed to classify the attention levels of ADHD subjects. The experimental cognitive task ‘Surround suppression’ includes two events: Stim ON and Stim OFF related to the high and low attention levels of a subject. In the proposed framework, ADHD-specific channels are initially identified from input Electroencephalography (EEG). Next, the significant, non-noisy connectivity features are extracted from those channels through the Singular Value Decomposition (SVD) method. Finally, the non-noisy features are passed to the robust Swin-T model for attention-level classification. The proposed model achieves 97.28% classification accuracy with 12 subjects. The robustness of the proposed model leads to potential benefits in EEG-based research and clinical settings, enhancing the reliability of ADHD assessments.

This dataset contains simultaneous EEG-fMRI recordings for inner speech experiments. Data were collected using a 3T MRI scanner and 64-channel BrainProducts EEG system. The EEG data have undergone preprocessing, including pulse artifact removal, using the BrainVision Analyzer software. No further data transformations have been applied to ensure the dataset remains BIDS-compliant as "raw".

The human brain needs fresh oxygen from the blood to work properly. A brain stroke happens when blood flow to the brain is blocked. Identifying brain strokes is one of the vital areas of medical research performed with medical imaging techniques. There exist popular medical imaging techniques such as X-ray, computed tomography (CT), positron emission tomography (PET), magnetic resonance imaging (MRI), and functional magnetic resonance imaging (fMRI) for stroke detection. Existing studies related to stroke detection are mainly based on algorithmic upgradation of different imaging techniques. Also, some of the review papers focused on the hardware and perfusion level upgradation of imaging methods separately. However, a fusion of the imaging techniques with hardware/perfusion has not been explored yet. To overcome these gaps, in this review, we deeply discuss various imaging techniques for stroke detection over time. In addition, this review also highlights the fusion in categorical-medical imaging techniques based on different imaging algorithms, imaging devices, and physio-chemical (perfusion) aspects. This fusion in algorithm, device, and physio-chemical levels provides good achievement concerning the segmentation of stroke region. The outcome of our review (in terms of fusion) is illustrated in a categorical tree format, which provides significant help to the interested researcher for accurate guidance.

Inner speech, or covert speech, refers to the internal generation of language without overt articulation. Decoding inner speech has significant implications for brain-computer interfaces (BCIs), particularly for assistive communication in individuals with speech and motor impairments. To facilitate research in this area, we introduce a publicly available dataset comprising simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data during inner speech production.Data were collected from three healthy, right-handed participants performing an inner speech task. The task involved silent repetition of visually presented words belonging to either a social or numerical category. The experiment consisted of 40 trials per word, with eight unique words and starts with a fixation period of two seconds. Stimuli were displayed for two seconds at the beginning of each session, followed by a 12-second rest period to allow hemodynamic responses to return to baseline. Participants were instructed to remain still and avoid movements to minimize artifacts.EEG was recorded using a 64-channel MR-compatible cap (BrainCap MR, EasyCap GmbH) at a 5 kHz sampling rate. Electrocardiogram (ECG) signals were simultaneously acquired using an additional electrode placed on the trapezius muscle to facilitate cardioballistic artifact correction. Gradient and cardioballistic artifacts were corrected using BrainVision Analyzer software.Functional MRI data were acquired using a 3T scanner with a 48-channel headcoil, and an echo-planar imaging (EPI) sequence optimized for whole-brain coverage. The repetition time (TR) was 2 s. High-resolution anatomical T1-weighted images were also acquired for structural reference. The dataset is publicly available in the OpenNeuro repository.The aim of this dataset is to provide a resource for studying inner speech processing, multimodal neuroimaging, EEG-fMRI fusion techniques, and BCI-driven speech prosthesis development.

The transformer model is excellent at handling time series signals (such as electroencephalography: EEG) because it can extract information from long-term dependencies effectively. This work combines binarization of EEG connectivity features, cognitive state classification using the vision transformer (ViT), and identifying graphical connectivity patterns for each cognitive state of the mental arithmetic task. The common spatial pattern (CSP) filter coefficient-based channel selection method selects the optimum EEG channels from the input channel set. Then, the Singular Value Decomposition (SVD) method is applied to prepare the binarized connectivity feature matrices, eliminating noisy connections between the optimum channels. The binarized functional-effective connectivity features are passed to the ViT model for cognitive state classification. The ViT model achieves the maximum classification accuracy of 94.86% with the phase-based connectivity feature. The proposed model improves classification accuracy by 6.15% compared to the state-of-the-art studies. This study also suggests a robust brain connectivity network to build a graphical connectivity pattern for each cognitive state. My findings of the EEG-based graphical patterns will bring further understanding of the scalp-level EEG channel patterns among different brain regions for other cognitive tasks.

Electroencephalography (EEG) is a fundamental tool in the non-invasive evaluation of brain activity, providing insights into the intricate dynamics at play within neurode-generative disorders. Conventional methodologies often lack in effectively capturing the temporal and intricate intra- and inter-channel dynamics, leading to diminished predictive accuracy. To address this problem, we present an innovative framework that effectively captures temporal along with intra- and inter-channel dynamics for EEG analysis aimed at predicting neu-rodegenerative disorders, explicitly targeting Alzheimer's and dementia. The proposed method involves constructing aggregated recurrence matrices from EEG channels followed by kernel formation and convolution operation, effectively encapsulating intra- and inter-channel spatiotemporal patterns, thereby achieving a more comprehensive representation of neural dynamics. The proposed approach was validated using public datasets, revealing competitive performance. Implementation details with codes will be accessible on GitHub.

Finding the synchronization between Electroencephalography (EEG) and human cognition is an essential aspect of cognitive neuroscience. Adaptive Control of Thought-Rational (ACT-R) is a widely used cognitive architecture that defines the cognitive and perceptual operations of the human mind. This study combines the ACT-R and EEG-based cortex-level connectivity to highlight the relationship between ACT-R modules during the EEG-based n-back task (for validating working memory performance). Initially, the source localization method is performed on the EEG signal, and the mapping between ACT-R modules and corresponding brain scouts (on the cortex surface) is performed. Once the brain scouts are identified for ACT-R modules, then those scouts are called ACT-R scouts. The linear (Granger Causality: GC) and non-linear effective connectivity (Multivariate Transfer Entropy: MTE) methods are applied over the scouts’ time series data. From the GC and MTE analysis, for all n-back tasks, information flow is observed from the visual-to-imaginal ACT-R scout for storing the visual stimuli (i.e., input letter) in short-term memory. For 2 and 3-back tasks, causal flow exists from imaginal to retrieval ACT-R scout and vice-versa. Causal flow from procedural to the imaginal ACT-R scout is also observed for all workload levels to execute the set of productions. Identifying the relationship among ACT-R modules through scout-level connectivity in the cortical surface facilitates the effects of human cognition in terms of brain dynamics.

Traditional high-dimensional electroencephalography (EEG) features (spectral or temporal) may not always attain satisfactory results in cognitive workload estimation. In contrast, deep representation learning (DRL) transforms high-dimensional data into cluster-friendly low-dimensional feature space. Therefore, this paper proposes an Integrated Spatio-Temporal Deep Clustering (ISTDC) model that uses DRL followed by a clustering method to achieve better clustering performance. The proposed model is illustrated using four Algorithms and Variational Bayesian Gaussian Mixture Model (VBGMM) clustering method. Temporal and spatial Variational Auto Encoder (VAE) models (mentioned in Algorithm 2 and Algorithm 3) learn temporal and spatial latent features from sequence-wise EEG signals and scalp topographical maps using the Long short-term memory and Convolutional Neural Network models. The concatenated spatio-temporal latent feature (mentioned in Algorithm 4) is passed to the VBGMM clustering method to efficiently estimate workload levels of -back task. For the 0-back vs. 2-back task, the proposed model achieves the maximum mean clustering accuracy of 98.0%, and it improves by 11.0% over the state-of-the-art method. The results also indicate that the proposed multimodal approach outperforms temporal and spatial latent feature-based unimodal models in workload assessment.

Understanding brain dynamics through connectivity networks is a growing topic of neuroscience. The volume conduction (VC) effect can be approximated as a linear mixing of the electrical fields of the brain regions, leading to spurious connectivity results. The proposed modularized brain connectivity network consists of three methods: Surface Laplacian (SL), partial correlation, and phase lag index (PLI) to eliminate VC effects from the brain connectivity network. SL is initially applied to the raw Electroencephalography (EEG) signal, and Event-related potential peak-wise modules for each EEG event are identified. Next, the optimum EEG channels are selected using the partial correlation method, and the source channel of each module is identified. Finally, the resultant brain connectivity network is constructed by adding the edges (i.e., PLI value) between the source channels of two modules. The experiment is performed on an EEG-based driving dataset. The performance of the proposed brain network for each driving event is evaluated based on graph measures such as mean local efficiency (MLE) and global efficiency (GE). After eliminating the VC effects, the modularized brain connectivity network significantly improves information processing rates (in terms of graph measures) across the brain region. We achieved maximum average GE (AGE) and average MLE (AMLE) values of 0.742 and 0.825 with the proposed brain network.