In real-world occupational settings, mental fatigue commonly emerges from the combination of sleep deprivation with prolonged cognitive and physical workload. However, this multidimensional fatigue profile is rarely captured in controlled experimental paradigms that examine brain activation and fatigue-related responses. Consequently, the validity and transferability of cognitive fatigue biomarkers identified in passive, laboratory-based fatigue paradigms to real-world active occupational conditions remain largely unexplored. This work investigates the functional connectivity of resting-state EEG in an active (real-world) and a passive (lab-induced) mental fatigue dataset, employing a multilayer graph representation in sensor-space that integrates both within-band and cross-frequency connectivity information. Specifically, functional connections within five EEG frequency bands are quantified using Phase Lag Index (PLI), while cross-frequency interactions between bands are characterized using Phase-Amplitude Coupling (PAC). Subsequently, a dataset-specific feature extraction pipeline is adopted, utilizing multiple machine learning classifiers to co-validate the most discriminative features, while the resulting network topologies are interpreted in light of established mental fatigue neurophysiological mechanisms. Our results promote the inter-frequency PAC metric as more informative for fatigue discrimination in both datasets, compared to the intra-frequency PLI, with smaller feature sets needed in the "active" vs. "passive" fatigue setting for information saturation. The topology of the selected feature sets indicates a reorganization of neural resources that is strictly dependent on the nature of fatigue induction.

Small-cell lung cancer (SCLC) is an aggressive malignancy that exhibits high prevalence for brain metastases. Furthermore, chemotherapy and metastasis-preventive approaches are also linked to neurotoxicity, further aggravating cognitive impairment. Despite evidence supporting structural and functional brain alterations in SCLC, the application of machine learning (ML) to new connectivity biomarkers has remained unexplored. This study is—to the best of our knowledge—the first to apply ML to structural brain connectomics in SCLC, using diffusion tensor imaging (DTI) to identify features discriminating between post-chemotherapy SCLC patients and healthy controls. Specifically, we constructed structural networks via deterministic tractography, applying an adapted feature reduction technique to identify the most informative connections without selection bias. This process isolated 16 connections involving 26 brain regions, predominantly in the frontal, temporal, and parietal lobes, showcasing primarily intra-hemispheric and left-lateralized alterations. Our optimal model leveraged a Gaussian Support Vector Machine (SVM), achieving a weighted accuracy of 0.92, a sensitivity of 0.93, a specificity of 0.91, and an area under the curve of 0.94. The selected feature subset retained high performance when tested with other classifiers, confirming its robustness. Our findings differ from prior studies based on statistically derived features, highlighting the ML-driven connectomics’ potential in uncovering DTI-derived SCLC patterns, offering interpretable insights for neuroimaging-based diagnostics.

This study harnesses EEG signals to enable the real-time control of servo motors, utilizing the OpenBCI Community Dataset to identify and assess brainwave patterns related to motor imagery tasks. Specifically, the dataset includes EEG data from 52 subjects, capturing electrical brain activity while participants imagined executing specific motor tasks. Each participant underwent multiple trials for each motor imagery task, ensuring a diverse and comprehensive dataset for model training and evaluation. A deep neural network model comprising convolutional and bidirectional long short-term memory (LSTM) layers was developed and trained using k-fold cross-validation, achieving a notable accuracy of 98%. The model’s performance was further compared against recurrent neural networks (RNNs), multilayer perceptrons (MLPs), and Τransformer algorithms, demonstrating that the CNN-LSTM model provided the best performance due to its effective capture of both spatial and temporal features. The model was deployed on a Python script interfacing with an Arduino board, enabling communication with two servo motors. The Python script predicts actions from preprocessed EEG data to control the servo motors in real-time. Real-time performance metrics, including classification reports and confusion matrices, demonstrate the seamless integration of the LSTM model with the Arduino board for precise and responsive control. An Arduino program was implemented to receive commands from the Python script via serial communication and control the servo motors, enabling accurate and responsive control based on EEG predictions. Overall, this study presents a comprehensive approach that combines machine learning, real-time implementation, and hardware interfacing to enable the precise and real-time control of servo motors using EEG signals, with potential applications in the human–robot interaction and assistive technology domains.

The efficient translation of brain signals into an output device is an essential characteristic to establish a Brain-computer Interface (BCI) link. This research investigates the applicability of diverse correlation indices for the differentiation of specific movements (left, right, both, or none) and states (real or imaginary) in a private BCI dataset, including EEG recordings of 32 participants. As such, the recorded brain activation data were employed to illustrate the differences between visual- and auditory-event-related responses during task performance. Our methodology involved a two-pronged approach. Firstly, EEG data were collected, capturing both the visual- and auditory-event-related signals that corresponded to each of the four movement classes. Secondly, we performed a comparative analysis of the collected dataset using various correlation algorithms, such as Pearson, Spearman, and Kendall, among others, to evaluate their effectiveness in differentiating between movements and states. The results demonstrated distinctive correlation patterns, as the selected indices effectively distinguished between real and imaginary movements, as well as between different lower limp movements in most cases. Moreover, the correlation schemas of certain individuals presented greater sensitivity in discerning nuances within the dataset. In this regard, it can be inferred that the chosen correlation indices can provide valuable insights into the aforementioned differentiation in EEG data. The results open up potential paths for improving BCI interfaces and contributing to more accurate prediction models.

This research presents a machine learning model that predicts left, right, or no action using electroencephalography (EEG) signals extracted from two different wearable EEG headsets. The research aims to develop an accurate and efficient model by following a rigorous and effective process divided into two parts. In Part I, the constant features approach is employed, which involves data loading, feature extraction, preprocessing, model selection, and tuning the best model for optimal performance. The performance of classification algorithms (support vector machine (SVM), decision tree classifier, and random forest classifier) is evaluated using root-mean-squared error metrics. In Part II, the multivariate time series approach is utilized to improve the accuracy and robustness of the model. The approach involves data loading, preprocessing (such as normalizing the data), modeling, results analysis, and deployment preparation. A neural network architecture consisting of convolutional filters followed by a long short-term memory neural network (LSTM) is used in the proposed approach. The convolutional layer performs a convolution of an input series of feature maps with a filter matrix to extract high-level features. The LSTM network is specifically designed to capture long-term dependencies and overcome the issue of vanishing gradients. The proposed approach achieves an accuracy of 98% and can be used for real-time testing. The model can be utilized in various fields where accurate and real-time prediction of brain–computer interfaces (BCI) actions is crucial. Overall, the proposed approach provides a promising solution to the problem of action prediction using EEG signals, and further research can be conducted to explore its potential applications and optimize its performance.

The detection of mental fatigue is an important issue in the nascent field of neuroergonomics. Although machine learning approaches and especially deep learning designs have constantly demonstrated their efficiency to automatically detect critical features from raw data, the computational resources for training and predictions are usually very demanding. In this work, we propose a shallow convolutional neural network, with three convolutional layers, for fatigue detection using electroencephalogram (EEG) data that can alleviate the computational burden and provide fast mental fatigue detection. As such, a deep learning model was created utilizing time-frequency domain features, extracted with Morlet wavelet analysis. These features, combined with the higher-level characteristics learnt by the model, resulted in a resilient solution, able to attain very high prediction accuracy (97%), while reducing training time and computing costs. Moreover, by incorporating a subsequent SHAP values analysis on the characteristics that contributed in the model creation, indications of low frequency (theta and alpha band) brain wave characteristics were indicated as prominent mental fatigue detectors.

OBJECTIVE:Electroencephalographic analysis (EEG) has emerged as a powerful tool for brain state interpretation. Studies have shown distinct deviances of patients with schizophrenia in EEG activation at specific frequency bands. METHODS:Evidence is presented for the validation of a Convolutional Neural Network (CNN) model using transfer learning for scalp EEGs of patients and controls during the performance of a speeded sensorimotor task and a working memory task. First, we trained a CNN on EEG data of 41 schizophrenia patients (SCZ) and 31 healthy controls (HC). Secondly, we used a pretrained model for training. Both models were tested in an external validation set of 15 SCZ, 16 HC, and 12 first-degree relatives. RESULTS:Using the layer-wise relevance propagation on the classification decision, a heatmap was produced for each subject, specifying the pixel-wise relevance. The CNN model resulted in the first case in a balanced accuracy of 63.7% and 81.5% in the second case, on the external validation test 64.5% and 83.2%, respectively. CONCLUSIONS:The theta and alpha frequency bands of the EEG signals had significant relevance to the CNN classification decision and predict the first-degree relatives indicating potential heritable functional deviances. SIGNIFICANCE:The proposed methodology results in important advancements for the identification of biomarkers in schizophrenia heritability.

Human memory comprises one of the most complex brain functions, attracting researchers to unveil the neural mechanisms governing its effective operation. In this respect, the current study examines the application of a wearable single-channel EEG to the interpretation of cognitive operations reflecting memory processes. For this purpose, we implemented a set of tasks for evaluating the participants’ processing skills and memory efficiency, in order to examine potential outcomes derived from a specialized cognitive training routine. The employed training method targeted the distinction of automatic and controlled processing and its effects on memory, while we also investigated transfer effects to untrained tasks. Based on the electrophysiological data recorded during the cognitive tasks, we computed measures of induced EEG activity for each frequency band to examine the influence of cognitive training on both task performance and brain activity, as well as whether the EEG metrics could provide insight into the underlying brain processes and augment the interpretation of behavioral outcomes. Ultimately, statistical analysis showed an apparent contribution of EEG in understanding the observed behavioral differences, while our training program had a clear impact on the participants’ performance and brain activity. Moreover, we observed the reported distinction between automatic and controlled memory processes which play an integral part in both ageing and cognitive impairments.

Brain source localization has been consistently implemented over the recent years to elucidate complex brain operations, pairing the high temporal resolution of the EEG with the high spatial estimation of the estimated sources. This review paper aims to present the basic principles of Electrical source imaging (ESI) in the context of the recent progress for solving the forward and the inverse problems, and highlight the advantages and limitations of the different approaches. As such, a synthesis of the current state-of-the-art methodological aspects is provided, offering a complete overview of the present advances with regard to the ESI solutions. Moreover, the new dimensions for the analysis of the brain processes are indicated in terms of clinical and cognitive ESI applications, while the prevailing challenges and limitations are thoroughly discussed, providing insights for future approaches that could help to alleviate methodological and technical shortcomings.

This paper investigates eye behaviour through blinks activity during stress conditions. Although eye blinking is a semi-voluntary action, it is considered to be affected by one’s emotional states such as arousal or stress. The blinking rate provides information towards this direction, however, the analysis on the entire eye aperture timeseries and the corresponding blinking patterns provide enhanced information on eye behaviour during stress conditions. Thus, two experimental protocols were established to induce affective states (neutral, relaxed and stress) systematically through a variety of external and internal stressors. The study populations included 24 and 58 participants respectively performing 12 experimental affective trials. After the preprocessing phase, the eye aperture timeseries and the corresponding features were extracted. The behaviour of inter-blink intervals (IBI) was investigated using the Markovian Analysis to quantify incidence dynamics in sequences of blinks. Moreover, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) network models were employed to discriminate stressed versus neutral tasks per cognitive process using the sequence of IBI. The classification accuracy reached a percentage of 81.3% which is very promising considering the unimodal analysis and the noninvasiveness modality used.

Motor-related Brain Computer Interfaces (BCIs) comprise a valuable tool towards the development of support systems targeting impaired individuals. Due to limited motion, classification training approaches mostly incorporate motor imagery paradigms, tapping into movement planning and preparation brain activity. In this context, subjects imagine executing specific motions through a target cue, while the corresponding electrophysiological activity is subsequently analyzed for movement prediction or early detection based on onset cognitive processes. Although previous studies have implemented a wide variety of classifiers and features for training, little emphasis has been given on the external trigger modality and its impact on subject-specific classification training. On that ground, we investigated differences in BCI training performance utilizing motion tasks guided via visual and auditory stimuli, testing multiple classifiers and frequency bands. Our results showed intra-subject as well as between-subject variability based on the stimulus, supporting the employment of multisensory guidance cues within BCI training protocols for optimal classification performance.

The cognitive processing and detection of errors is important in the adaptation of the behavioral and learning processes. This brain activity is often reflected as distinct patterns of event-related potentials (ERPs) that can be employed in the detection and interpretation of the cerebral responses to erroneous stimuli. However, high-accuracy cross-condition classification is challenging due to the significant variations of the error-related ERP components (ErrPs) between complexity conditions, thus hindering the development of error recognition systems. In this study, we employed support vector machines (SVM) classification methods, based on waveform characteristics of ErrPs from different time windows, to detect correct and incorrect responses in an audio identification task with two conditions of different complexity. Since the performance of the classifiers usually depends on the salience of the features employed, a combination of the sequential forward floating feature selection (SFFS) and sequential forward feature selection (SFS) methods was implemented to detect condition-independent and condition-specific feature subsets. Our framework achieved high accuracy using a small subset of the available features both for cross- and within-condition classification, hence supporting the notion that machine learning techniques can detect hidden patterns of ErrP-based features, irrespective of task complexity while additionally elucidating complexity-related error processing variations. Graphical abstract A schematic of the proposed approach. (a) EEG recordings in an auditory experiment in two conditions of different complexity. (b) Characteristic event related activity feature extraction. (c) Selection of feature vector subsets for easy and hard conditions corresponding to correct (Class1) and incorrect (Class2) responses. (d) Performance for individual and cross-condition classification.

The use of computer-based examination systems offers advantages related to the reduction of human resource allocation and to gains in objectivity for the scoring process. Multiple-choice questions (MCQs) are widely used in such systems; one of the main reasons that hamper their effectiveness, in comparison with constructed response questions (CRQ), is the influence of guessing. Considering limitations within previously proposed MCQs examination methods and scoring rules, in the present work a novel MCQs examination method is presented, termed 'adaptive' MCQs method. MCQs are divided into 3 categories, per difficulty level. The `path' that an examinee will follow is constituted by 3 phases, wherein a set of questions belonging to one of the three difficulty-categories, is appointed. The exact path followed is selected per the success level of the examinee in the preceding phase. The scoring provided by the adaptive MCQs examination method produced results that were statistically indistinguishable to the scoring produced by a traditional CRQ examination method. At the same time, both the scoring results of the adaptive MCQs examination and the scoring results of the CRQ examination differed significantly from those obtained by a generic `non-adaptive' MCQs examination.

Sleep spindles are rhythmic transient waveforms present in the electroencephalogram (EEG) of non-rapid eye movement (NREM) sleep. In the present study a period-amplitude analysis method was applied for the automated detection of sleep spindles in all-night sleep EEG recordings of young healthy subjects. The method relies on the characterization of individual half-waves of the EEG data, by estimating electrographic parameters such as amplitude and duration and by assigning a grade to each half-wave depending on where it lies in the amplitude-frequency plane. The grading is followed by the detection system, checking consecutive half-wave characteristics and implementing a set of rules for determining the start and the end of spindle bursts and for retaining or rejecting sleep spindle indications provided during the various stages of the detection system. The sensitivity and false positive rate across subjects was 78.9% and 10.9%, respectively, providing indication that the method could be successfully applied to larger sets of healthy subjects of various age groups, as well as to patient populations.

'The study of eye movements has been increasing over the past decade. It is considered that eye movements, mainly saccades and blinks, provide significant information for cognitive and visual processes of the observers. Saccades and blinks are high velocity eye movements. In this paper, the automatic identification of saccades and blinks, as well as their onset and offset, is proposed based on a novel implementation of nonlinear dynamics using the Largest Lyapunov Exponent and the logarithm of the divergence. The Largest Lyapunov Exponent detection method was tested on 25,000 saccades and 2,366 blinks, detecting with high accuracy and precision both types of eye movements. The Largest Lyapunov Exponent detection method was compared against two other existing techniques for blink and saccade identification, showing advantageous performance. (C) 2017 Elsevier Ltd. All rights reserved.

The detection of an error is the cognitive evaluation of an action outcome that is considered undesired or mismatches an expected response. Brain activity during monitoring of correct and incorrect responses elicits Event Related Potentials (ERPs) revealing complex cerebral responses to deviant sensory stimuli. Development of accurate error detection systems is of great importance both concerning practical applications and in investigating the complex neural mechanisms of decision making. In this study, data are used from an audio identification experiment that was implemented with two levels of complexity in order to investigate neurophysiological error processing mechanisms in actors and observers. To examine and analyse the variations of the processing of erroneous sensory information for each level of complexity we employ Support Vector Machines (SVM) classifiers with various learning methods and kernels using characteristic ERP time-windowed features. For dimensionality reduction and to remove redundant features we implement a feature selection framework based on Sequential Forward Selection (SFS). The proposed method provided high accuracy in identifying correct and incorrect responses both for actors and for observers with mean accuracy of 93% and 91% respectively. Additionally, computational time was reduced and the effects of the nesting problem usually occurring in SFS of large feature sets were alleviated.

Multiple Choice Question (MCQs) tests have long been used as assessment tools. A significant problem of MCQs tests is the positive grades bias introduced by guessing. This chapter describes a novel assessment tool in which the questions given in the test constitute pairs, referred to as “paired” MCQs (p-MCQs). Every pair addresses the same topic, but this similarity is not evident for a student who does not possess adequate knowledge on the topic addressed in the questions of the pair. The answers to the questions are graded in pairs, providing a bonus, if both questions of the pair are correctly answered, or a penalty, if only one of the pair's questions is answered correctly. If both answers are wrongly answered no marking is collected by the examinee. Application examples are reviewed, indicating that the p-MCQs method removes the guessing factor bias, in a way that might not overtly induce the dissuading effects of direct negative marking of incorrect answers, commonly used in mixed-scoring schemes.

The frequency of intrusive saccades during maintenance of active visual fixation has been used as a measure of sustained visual attention in studies of healthy subjects as well as of neuropsychiatric patient populations. In this study, the mechanism that generates intrusive saccades during active visual fixation was investigated in a population of young healthy men performing three sustained fixation tasks (fixation to a visual target, fixation to a visual target with visual distracters, and fixation straight ahead in the dark). Markov Chain modeling of inter-saccade intervals (ISIs) was utilized. First- and second-order Markov modeling provided indications for the existence of a non-random pattern in the production of intrusive saccades. Accordingly, the system of intrusive saccade generation may operate in two "attractor" states, one in which intrusive saccades occur at short consecutive ISIs and another in which intrusive saccades occur at long consecutive ISIs. These states might correspond to two distinct states of the attention system, one of low focused high distractibility and another of high focused low distractibility, such as those proposed in the adaptive gain theory for the control of attention by the noradrenergic system in the brain. To the authors knowledge, this is the first time that Markov Chain modeling has been applied to the analysis of the ISIs of intrusive sac cades. (C) 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

The identification of correct or incorrect actions is a very significant task in the field of the brain-computer interface systems. In this paper, observations of correct or incorrect actions are identified by means of event related potentials (ERPs) that represent the brain activity as a response to an external stimulus or event. ERP signals from 47 electrodes, located on various positions on the scalp, were acquired from sixteen volunteers. The volunteers observed correct or incorrect actions of other subjects, who performed a special designed task. The recorded signals were analysed and five second order statistical features were calculated from each one. The most prominent features were selected using a statistical ranking procedure forming a set of 32 feature vectors, which were fed to a Support Vector Machines (SVM) classifier. The performance of the classifier was assessed by means of the leave-one-out cross validation procedure resulting in classification accuracy 84.4%. The obtained results indicate that the analysis of ERP-signals that are collected during the observation of the actions of other persons could be used to understand the specific cognitive processes that are responsible for processing the observed actions.