Classification Of Mental States Research Articles

Towards unlocking motor control in spinal cord injured by applying an online EEG-based framework to decode motor intention, trajectory and error processing

Brain-computer interfaces (BCIs) can translate brain signals directly into commands for external devices. Electroencephalography (EEG)-based BCIs mostly rely on the classification of discrete mental states, leading to unintuitive control. The ERC-funded project "Feel Your Reach" aimed to establish a novel framework based on continuous decoding of hand/arm movement intention, for a more natural and intuitive control. Over the years, we investigated various aspects of natural control, however, the individual components had not yet been integrated. Here, we present a first implementation of the framework in a comprehensive online study, combining (i) goal-directed movement intention, (ii) trajectory decoding, and (iii) error processing in a unique closed-loop control paradigm. Testing involved twelve able-bodied volunteers, performing attempted movements, and one spinal cord injured (SCI) participant. Similar movement-related cortical potentials and error potentials to previous studies were revealed, and the attempted movement trajectories were overall reconstructed. Source analysis confirmed the involvement of sensorimotor and posterior parietal areas for goal-directed movement intention and trajectory decoding. The increased experiment complexity and duration led to a decreased performance than each single BCI. Nevertheless, the study contributes to understanding natural motor control, providing insights for more intuitive strategies for individuals with motor impairments.

A Functional Region Decomposition Method to Enhance fNIRS Classification of Mental States

Functional near-infrared spectroscopy (fNIRS) classification of mental states is of important significance in many neuroscience and clinical applications. Existing classification algorithms use all signal-collected brain regions as a whole, and brain sub-region contributions have not been well investigated. This paper proposes a functional region decomposition (FRD) method to incorporate brain sub-region contributions and enhance fNIRS classification of mental states. Specifically, the method iteratively decomposes the brain region into multiple sub-regions to maximize their contributions with respect to the validation accuracy and coverage of brain sub-regions. Then for the fNIRS data in brain sub-regions, features are extracted and classified to output the predictions. The final predictions are determined by fusing predictions from multiple brain sub-regions with stacking. Experiments on a publicly available fNIRS dataset showed that the proposed functional region decomposition method led to 9.01% and 10.58% increase of classification accuracy for the methods related to slope-based features and mean concentration change features, respectively. Therefore, the proposed method can decompose the brain region into sub-regions with respect to their functional contributions and fundamentally enhance the performance of mental state classification.

Functional Connectivity Ensemble Method to Enhance BCI Performance (FUCONE).

Relying on the idea that functional connectivity provides important insights on the underlying dynamic of neuronal interactions, we propose a novel framework that combines functional connectivity estimators and covariance-based pipelines to improve the classification of mental states, such as motor imagery. A Riemannian classifier is trained for each estimator and an ensemble classifier combines the decisions in each feature space. A thorough assessment of the functional connectivity estimators is provided and the best performing pipeline among those tested, called FUCONE, is evaluated on different conditions and datasets. Using a meta-analysis to aggregate results across datasets, FUCONE performed significantly better than all state-of-the-art methods. The performance gain is mostly imputable to the improved diversity of the feature spaces, increasing the robustness of the ensemble classifier with respect to the inter- and intra-subject variability. Our results offer new insights into the need to consider functional connectivity-based methods to improve the BCI performance.

Classification of Relaxation and Concentration Mental States with EEG

In this paper, we study the use of EEG (Electroencephalography) to classify between concentrated and relaxed mental states. In the literature, most EEG recording systems are expensive, medical-graded devices. The expensive devices limit the availability in a consumer market. The EEG signals are obtained from a toy-grade EEG device with one channel of output data. The experiments are conducted in two runs, with 7 and 10 subjects, respectively. Each subject is asked to silently recite a five-digit number backwards given by the tester. The recorded EEG signals are converted to time-frequency representations by the software accompanying the device. A simple average is used to aggregate multiple spectral components into EEG bands, such as α, β, and γ bands. The chosen classifiers are SVM (support vector machine) and multi-layer feedforward network trained individually for each subject. Experimental results show that features, with α+β+γ bands and bandwidth 4 Hz, the average accuracy over all subjects in both runs can reach more than 80% and some subjects up to 90+% with the SVM classifier. The results suggest that a brain machine interface could be implemented based on the mental states of the user even with the use of a cheap EEG device.

Questioning Searle’s Explanation of Unconscious Mental State

Upholding the principle of Biological Naturalism, John Searle requires that explanation of mental phenomenon must be coherent with findings in scientific studies. To meet the requirement of coherence, Searle puts forward his “connection principle”. This paper contends that his requirement is not well met in his own study of unconscious mental states. Firstly, his classification of unconscious mental states is not reasonable. For example, he has classified “knowledge of language” into deep unconscious mental state, which in nature belongs to nonconscious mental state. The classification is not in accord with findings in linguistic studies, nor with linguistic practice. Secondly, his explanation for the unconscious mental process is doubtful. Based on his “connection principle”, unconscious mental state is nothing but potential conscious mental state and therefore the processing of unconscious mental activity should resemble that of conscious mental activity. But this explanation is not coherent with findings in cognitive science. The paper concludes that Searle fails in his requirement of coherence.

EEG Feature Extraction Using Genetic Programming for the Classification of Mental States

The design of efficient electroencephalogram (EEG) classification systems for the detection of mental states is still an open problem. Such systems can be used to provide assistance to humans in tasks where a certain level of alertness is required, like in surgery or in the operation of heavy machines, among others. In this work, we extend a previous study where a classification system is proposed using a Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) for the classification of two mental states, namely a relaxed and a normal state. Here, we propose an enhanced feature extraction algorithm (Augmented Feature Extraction with Genetic Programming, or +FEGP) that improves upon previous results by employing a Genetic-Programming-based methodology on top of the CSP. The proposed algorithm searches for non-linear transformations that build new features and simplify the classification task. Although the proposed algorithm can be coupled with any classifier, LDA achieves 78.8% accuracy, the best predictive accuracy among tested classifiers, significantly improving upon previously published results on the same real-world dataset.

A LightGBM-Based EEG Analysis Method for Driver Mental States Classification.

Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is based on gradient boosting framework for EEG mental states identification. The comparable results with traditional classifiers, such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI).

Switch or stay? Automatic classification of internal mental states in bistable perception

The human brain goes through numerous cognitive states, most of these being hidden or implicit while performing a task, and understanding them is of great practical importance. However, identifying internal mental states is quite challenging as these states are difficult to label, usually short-lived, and generally, overlap with other tasks. One such problem pertains to bistable perception, which we consider to consist of two internal mental states, namely, transition and maintenance. The transition state is short-lived and represents a change in perception while the maintenance state is comparatively longer and represents a stable perception. In this study, we proposed a novel approach for characterizing the duration of transition and maintenance states and classified them from the neuromagnetic brain responses. Participants were presented with various types of ambiguous visual stimuli on which they indicated the moments of perceptual switches, while their magnetoencephalogram (MEG) data were recorded. We extracted different spatio-temporal features based on wavelet transform, and classified transition and maintenance states on a trial-by-trial basis. We obtained a classification accuracy of 79.58% and 78.40% using SVM and ANN classifiers, respectively. Next, we investigated the temporal fluctuations of these internal mental representations as captured by our classifier model and found that the accuracy showed a decreasing trend as the maintenance state was moved towards the next transition state. Further, to identify the neural sources corresponding to these internal mental states, we performed source analysis on MEG signals. We observed the involvement of sources from the parietal lobe, occipital lobe, and cerebellum in distinguishing transition and maintenance states. Cross-conditional classification analysis established generalization potential of wavelet features. Altogether, this study presents an automatic classification of endogenous mental states involved in bistable perception by establishing brain-behavior relationships at the single-trial level.

EEG classification of driver mental states by deep learning.

Driver fatigue is attracting more and more attention, as it is the main cause of traffic accidents, which bring great harm to society and families. This paper proposes to use deep convolutional neural networks, and deep residual learning, to predict the mental states of drivers from electroencephalography (EEG) signals. Accordingly we have developed two mental state classification models called EEG-Conv and EEG-Conv-R. Tested on intra- and inter-subject, our results show that both models outperform the traditional LSTM- and SVM-based classifiers. Our major findings include (1) Both EEG-Conv and EEG-Conv-R yield very good classification performance for mental state prediction; (2) EEG-Conv-R is more suitable for inter-subject mental state prediction; (3) EEG-Conv-R converges more quickly than EEG-Conv. In summary, our proposed classifiers have better predictive power and are promising for application in practical brain-computer interaction .

Eye state classification from electroencephalography recordings using machine learning algorithms

Background and Objectives: Current developments in electroencephalography (EEG) foster medical and nonmedical applications outside the hospitals. For example, continuous monitoring of mental and cognitive states can contribute to avoid critical and potentially dangerous situations in daily life. An important prerequisite for successful EEG at home is a real-time classification of mental states. In this article, we compare different machine learning algorithms for the classification of eye states based on EEG recordings. Materials and Methods: We tested 23 machine learning algorithms from the Waikato Environment for Knowledge Analysis toolkit. Each classifier was analyzed on four different datasets, since two separate approaches - called sample-wise and segment-wise - in combination with raw and filtered data were applied. These datasets were recorded for 27 volunteers. The different approaches are compared in terms of accuracy, complexity, training time, and classification time. Results: Ten out of 23 classifiers fulfilled the determined requirements of high classification accuracy and short time of classification and can be denoted as applicable for real-time EEG eye state classification. Conclusions: We found that it is possible to predict eye states using EEG recordings with an accuracy from about 96% to over 99% in a real-time system. On the other hand, we found no best, universal method of classifying EEG eye states in all volunteers. Therefore, we conclude that the best algorithm should be chosen individually, using the optimal classification accuracy in combination with time of classification as the criterion.

8 Brain reading

David Linden read medicine, classics and philosophy in Germany. He obtained a DPhil from the University of Oxford for his work on medical ethics in antiquity in 2000 and a Dr. med. in neuroscience at the Max Planck Institute for Brain Research in 1999. He trained in psychiatry at Frankfurt University and has a special interest in clinical neuropsychiatry and neurodevelopmental genetic syndromes. Since 2011 he has been Professor of Translational Neuroscience at Cardiff University and Head of the Neuroimaging theme group of the MRC Centre for Neuropsychiatric Genetics and Genomics. He is also the director for clinical research at the Cardiff University Brain Research Imaging Centre. In his research, he applies structural and functional magnetic resonance imaging (MRI), neurophysiological techniques and genetics in order to understand the function of the brain in health and disease. Current research interests include functional imaging of psychopathology, neural substrates of social cognition and decision making, genetic imaging, treatment and training effects on the brain, and neurofeedback. David is the author of “The Biology of Psychological Disorders” (2012), “Brain Control” (2014) and “Neuroimaging and Neurophysiology in Psychiatry” (2016) and over 180 scientific papers in the fields of biological psychiatry, functional brain imaging, visual and social cognitive neuroscience, and neurofeedback. He has coordinated trials of functional MRI-based neurofeedback in depression and Parkinson’s disease. He is also the coordinator of the European Consortium “BRAINTRAIN” (www.braintrainproject.eu), funded by the European Commission under the 7th Framework Programme, which develops and evaluates imaging-based neurofeedback methods for a range of mental disorders. He is a consultant psychiatrist with the Cardiff and Vale University Health Board and provides clinics at the Welsh Neuropsychiatric Service, the Cardiff University Psychiatric Service (specialising in neurodevelopmental genetic syndromes) and the Welsh Huntington’s Disease Service. He has been a member of the board of directors of the BNPA since 2014. The techniques of brain imaging and neurophysiology (both invasive and non-invasive) hold considerable promise for the understanding of human mental processes. One branch of neurophysiology and cognitive neuroscience research is concerned with the inference of particular mental states from neural activation patterns. One of the aims of this research is to identify intentions in patients with severe communication disorders (for example the “locked-in” state). Another proposed application is in the distinction between disorders of consciousness and disorders of communication. Recent advances in the analysis of functional brain images that allow for the classification of mental states also raise the question whether these techniques can be used to underpin psychiatric diagnoses, possibly even support or refute patients’ claim about their own mental states. This talk discusses the clinical promise and also the technical and ethical limitations of these approaches and puts them in the context of the history and future of neuropsychiatry. References . David Linden. Brain Control – Developments in Therapy and Implications for Society. Palgrave Macmillan 2014. . Sarah Richmond, Geraint Rees, Sarah JL, Edwards (eds). I know what you’re thinking – Brain imaging and mental privacy. Oxford University Press 2012.

К вопросу о психологическом обеспечении профессиональной деятельности сотрудников правоохранительных органов с психическими дисфункциональными состояниями

In article the analysis of essence of the concept of mental dysfunctional state law enforcement officers, forming in the course of professional activity and its significance in the system of psychological support of the proposed classification of the typical dysfunctional mental States, defined basic criteria for evaluating mental dysfunctional States and test methods for the diagnostic evaluation of qualitative and quantitative indicators. The necessity to determine the level of activity and the polarity of mental dysfunctional States in order to develop a differentiated approach for development of further corrective measures. The authors recommended the most optimal method of psychological influence in the course of psychological support of professional activity of law enforcement officers with mental dysfunctional States. Shows the main advantages and features of socio-psychological training in comparison with the known traditional methods of psychotherapy.

An interval type-2 fuzzy approach for real-time EEG-based control of wrist and finger movement

Abstract Feature extraction and automatic classification of mental states is an interesting and open area of research in the field of brain–computer interfacing (BCI). A well-trained classifier would allow the BCI system to control an external assistive device in real world problems. Sometimes, standard existing classifiers fail to generalize the components of a non-stationary signal, like Electroencephalography (EEG) which may pose one or more problems during real-time usage of the BCI system. In this paper, we aim to tackle this issue by designing an interval type-2 fuzzy classifier which deals with the uncertainties of the EEG signal over various sessions. Our designed classifier is used to decode various movements concerning the wrist (extension and flexion) and finger (opening and closing of a fist). For this purpose, we have employed extreme energy ratio (EER) to construct the feature vector. The average classification accuracy achieved during offline training and online testing over eight subjects are 86.45% and 78.44%, respectively. On comparison with other related works, it is shown that our designed IT2FS classifier presents a better performance.

Localization of neural efficiency of the mathematically gifted brain through a feature subset selection method.

Based on the neural efficiency hypothesis and task-induced EEG gamma-band response (GBR), this study investigated the brain regions where neural resource could be most efficiently recruited by the math-gifted adolescents in response to varying cognitive demands. In this experiment, various GBR-based mental states were generated with three factors (level of mathematical ability, task complexity, and short-term learning) modulating the level of neural activation. A feature subset selection method based on the sequential forward floating search algorithm was used to identify an "optimal" combination of EEG channel locations, where the corresponding GBR feature subset could obtain the highest accuracy in discriminating pairwise mental states influenced by each experiment factor. The integrative results from multi-factor selections suggest that the right-lateral fronto-parietal system is highly involved in neural efficiency of the math-gifted brain, primarily including the bilateral superior frontal, right inferior frontal, right-lateral central and right temporal regions. By means of the localization method based on single-trial classification of mental states, new GBR features and EEG channel-based brain regions related to mathematical giftedness were identified, which could be useful for the brain function improvement of children/adolescents in mathematical learning through brain-computer interface systems.

Improvement of BCI Performance Through Nonlinear Independent Component Analysis Extraction

Electroencephalogram (EEG) recordings provide an important means of brain-computer communication, but their classification accuracy and transfer rate are limited by unexpected signal variations due to artifacts and noises. In this paper, a nonlinear independent component analysis (NICA) extraction method for brain signal based EEG-P300 are proposed. The performance of the proposed method is investigated through a comparison of well known extraction methods (i.e., AAR, JADE, and SOBI algorithms). Finally, the promising results reported here reflect the considerable potential of EEG for the continuous classification of mental states.

A Comparison of EEG Processing Methods to Improve the Performance of BCI

Electroencephalogram (EEG) recordings provide an important means of brain-computer communication, but their classification accuracy is limited by unforeseeable signal variations due to artifacts or recognizer-subject feedback. In this paper, we propose a comparison of processing method (i.e., NPCA, JADE, and SOBI) entailing time-series EEG signals. Finally, the promising results reported here (up to 94% average classification accuracy and 36.4% improvement of maximum average transfer rate) reflect the considerable potential of EEG for the continuous classification of mental states. 

Classification of Mental States Based on Spatiospectral Patterns of Brain Electrical Activity

Abstract: Classification of human thought is an emerging research field that may allow us to understand human brainfunctions and further develop advanced brain-computer interface (BCI) systems. In the present study, we introducea new approach to classify various mental states from noninvasive electrophysiological recordings of human brainactivity. We utilized the full spatial and spectral information contained in the electroencephalography (EEG) signalsrecorded while a subject is performing a specific mental task. For this, the EEG data were converted into a 2D spa-tiospectral pattern map, of which each element was filled with 1, 0, and -1 reflecting the degrees of event-relatedsynchronization (ERS) and event-related desynchronization (ERD). We evaluated the similarity between a current(input) 2D pattern map and the template pattern maps (database), by taking the inner-product of pattern matrices.Then, the current 2D pattern map was assigned to a class that demonstrated the highest similarity value. For theverification of our approach, eight participants took part in the present study; their EEG data were recorded whilethey performed four different cognitive imagery tasks. Consistent ERS/ERD patterns were observed more frequentlybetween trials in the same class than th ose in different classes, indicating that th ese spatiospectral pattern maps couldbe used to classify different mental states. The classification accuracy was evaluated for each participant from boththe proposed approach and a conventional mental state classification method based on the inter-hemispheric spectralpower asymmetry, using the leave-one-out cross-validation (LOOCV). An average accuracy of 68.13% (± 9.64%) wasattained for the proposed method; whereas an average accuracy of 57% (± 5.68%) was attained for the conventionalmethod (significance was assessed by the one-tail paired t-test, p < 0.01), showing that the proposed simple clas-sification approach might be one of the promising methods in discriminating various mental states.Key words:brain-computer interface (BCI), spatiospectral pattern, mental state classification, mind reading, elec-troencephalography (EEG)

What Makes a Pattern? Matching Decoding Methods to Data in Multivariate Pattern Analysis

Research in neuroscience faces the challenge of integrating information across different spatial scales of brain function. A promising technique for harnessing information at a range of spatial scales is multivariate pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data. While the prevalence of MVPA has increased dramatically in recent years, its typical implementations for classification of mental states utilize only a subset of the information encoded in local fMRI signals. We review published studies employing multivariate pattern classification since the technique’s introduction, which reveal an extensive focus on the improved detection power that linear classifiers provide over traditional analysis techniques. We demonstrate using simulations and a searchlight approach, however, that non-linear classifiers are capable of extracting distinct information about interactions within a local region. We conclude that for spatially localized analyses, such as searchlight and region of interest, multiple classification approaches should be compared in order to match fMRI analyses to the properties of local circuits.

Psychophysiological classification and staging of mental states during meditative practice

The study of meditation offers a perfect setting for the study of a large variety of states of consciousness. Here, we present a classification paradigm that can be used for staging of individual meditation sessions into a variety of predefined mental states. We have measured 64 channels of the electroencephalogram (EEG) plus peripheral physiological measures in 49 participants with varying experiences in meditation practice. The data recorded in a meditation session of seven meditative tasks were analyzed with respect to EEG power spectral density measures plus peripheral measures. A multiclass linear discriminant analysis classifier was trained for classification of data epochs of the seven standard tasks. The classification results were verified using random partitions of the data. As an overall result, about 83% (±7%) of the epochs could be correctly classified to their originating task. The best classification method was then applied to individual meditation sessions, which allowed for staging of meditation states similarly to the staging possibility of sleep states. This study exemplarily demonstrates the possibility of developing an automatized staging tool that can be used for monitoring changes in the states of consciousness offline or online for training or therapeutic purpose.

Classification methods for ongoing EEG and MEG signals.

Classification algorithms help predict the qualitative properties of a subject's mental state by extracting useful information from the highly multivariate non-invasive recordings of his brain activity. In particular, applying them to Magneto-encephalography (MEG) and electro-encephalography (EEG) is a challenging and promising task with prominent practical applications to e.g. Brain Computer Interface (BCI). In this paper, we first review the principles of the major classification techniques and discuss their application to MEG and EEG data classification. Next, we investigate the behavior of classification methods using real data recorded during a MEG visuomotor experiment. In particular, we study the influence of the classification algorithm, of the quantitative functional variables used in this classifier, and of the validation method. In addition, our findings suggest that by investigating the distribution of classifier coefficients, it is possible to infer knowledge and construct functional interpretations of the underlying neural mechanisms of the performed tasks. Finally, the promising results reported here (up to 97% classification accuracy on 1-second time windows) reflect the considerable potential of MEG for the continuous classification of mental states.