Hypnosis Measuring: The Pilot Study of Machine Learning Approach for Instrumental Control of the Trance Dynamics in Patients with Anxiety and Depressive Disorders

Having measurable physiological correlates, hypnosis should be measurable generally itself. The precise, continual, quantitative assessment (versus phenomenological one) of a current trance level (i.e., "depth") is possible only instrumentally. We've shown that electrophysiological patterns of a trance are stable from session to session, but significantly vary among subjects. Hence, to measure the trance level individually we proposed the following Brain-Computer interface approach and tested it on the 27 video-EEG recordings of 8 outpatients with anxiety and depressive disorders: on the data of the first session using Common Spatial Pattern filtering and Linear Discriminant Analysis classification, we trained the predictive models to discriminate conditions of "a wakefulness" and "a deep trance" and applied them to the subsequent sessions to predict the deep trance probability (in fact, to measure the trance level). We obtained integrative individualized continuously changing parameter reflecting the hypnosis level graphically online, providing the trance microdynamics control. The classification accuracy was high, especially while filtering the signal in 1.5-14 and 4-15 Hz. The applications and perspectives are being discussed.

Evaluating the Motor Imagery Classification Performance of a Double-Layered Feature Selection on Two Different-Sized Datasets

Numerous investigations have been conducted to enhance the motor imagery-based brain–computer interface (BCI) classification performance on various aspects. However, there are limited studies comparing their proposed feature selection framework performance on both objective and subjective datasets. Therefore, this study aims to provide a novel framework that combines spatial filters at various frequency bands with double-layered feature selection and evaluates it on published and self-acquired datasets. Electroencephalography (EEG) data are preprocessed and decomposed into multiple frequency sub-bands, whose features are then extracted, calculated, and ranked based on Fisher’s ratio and minimum-redundancy-maximum-relevance (mRmR) algorithm. Informative filter banks are chosen for optimal classification by linear discriminative analysis (LDA). The results of the study, firstly, show that the proposed method is comparable to other conventional methods through accuracy and F1-score. The study also found that hand vs. feet classification is more discriminable than left vs. right hand (4–10% difference). Lastly, the performance of the filter banks common spatial pattern (FBCSP, without feature selection) algorithm is found to be significantly lower (p = 0.0029, p = 0.0015, and p = 0.0008) compared to that of the proposed method when applied to small-sized data.

EEG-based Measurement System for Student Engagement Detection in Learning 4.0

A wearable system for the personalized EEG-based detection of engagement in learning 4.0 is proposed. The system can be used to make an automated teaching platform adaptable to the cognitive and emotional conditions of the user. For example, the teaching strategy could be personalized by an automatic modulation of the proposed contents. The system is validated by an experimental case study on twenty-one students. The experimental task consisted in learning how a specific human-machine interface works. Both the cognitive and motor skills of participants were involved. De facto standard stimuli based on cognitive task and music background were employed to guarantee a metrologically founded reference. The proposed signal processing pipeline (Filter bank, Common Spatial Pattern, and Support Vector Machine), in within-subject approach, reaches almost 77 % average accuracy by a 3 s time window, in detecting both cognitive and emotional engagement.

Dimension Reduction Using New Bond Graph Algorithm and Deep Learning Pooling on EEG Signals for BCI

One of the main challenges in studying brain signals is the large size of the data due to the use of many electrodes and the time-consuming sampling. Choosing the right dimensional reduction method can lead to a reduction in the data processing time. Evolutionary algorithms are one of the methods used to reduce the dimensions in the field of EEG brain signals, which have shown better performance than other common methods. In this article, (1) a new Bond Graph algorithm (BGA) is introduced that has demonstrated better performance on eight benchmark functions compared to genetic algorithm and particle swarm optimization. Our algorithm has fast convergence and does not get stuck in local optimums. (2) Reductions of features, electrodes, and the frequency range have been evaluated simultaneously for brain signals (left-handed and right-handed). BGA and other algorithms are used to reduce features. (3) Feature extraction and feature selection (with algorithms) for time domain, frequency domain, wavelet coefficients, and autoregression have been studied as well as electrode reduction and frequency interval reduction. (4) First, the features/properties (algorithms) are reduced, the electrodes are reduced, and the frequency range is reduced, which is followed by the construction of new signals based on the proposed formulas. Then, a Common Spatial Pattern is used to remove noise and feature extraction and is classified by a classifier. (5) A separate study with a deep sampling method has been implemented as feature selection in several layers with functions and different window sizes. This part is also associated with reducing the feature and reducing the frequency range. All items expressed in data set IIa from BCI competition IV (the left hand and right hand) have been evaluated between one and three channels, with better results for similar cases (in close proximity). Our method demonstrated an increased accuracy by 5 to 8% and an increased kappa by 5%.

Discriminating three motor imagery states of the same joint for brain-computer interface

The classification of electroencephalography (EEG) induced by the same joint is one of the major challenges for brain-computer interface (BCI) systems. In this paper, we propose a new framework, which includes two parts, feature extraction and classification. Based on local mean decomposition (LMD), cloud model, and common spatial pattern (CSP), a feature extraction method called LMD-CSP is proposed to extract distinguishable features. In order to improve the classification results multi-objective grey wolf optimization twin support vector machine (MOGWO-TWSVM) is applied to discriminate the extracted features. We evaluated the performance of the proposed framework on our laboratory data sets with three motor imagery (MI) tasks of the same joint (shoulder abduction, extension, and flexion), and the average classification accuracy was 91.27%. Further comparison with several widely used methods showed that the proposed method had better performance in feature extraction and pattern classification. Overall, this study can be used for developing high-performance BCI systems, enabling individuals to control external devices intuitively and naturally.