arXiv subjects

Grzegorz M. Wojcik

Publications and source records attributed to Grzegorz M. Wojcik.

6 recordsLinked to original sources

arXiv2026

EEG-Based Classification of Hypnotic Susceptibility Using Two-Dimensional Convolutional Neural Networks

Guided Imagery (GI) is a mind-based relaxation technique widely used in clinical and therapeutic settings. Objectively assessing whether a patient has entered a relaxed state remains a challenge for clinicians. This study investigates electroencephalography (EEG)-based classification of hypnotic susceptibility using two-dimensional Convolutional Neural Networks (2D CNNs) applied to minimally preprocessed signals, without manual feature extraction. Data were collected from 62 male participants (32 Control, 30 Hypno) using a 256-channel dense array amplifier during Guided Imagery or cognitive task conditions. A sliding-window framework evaluated up to 868 independent models across seven anatomically defined brain regions and 124 ten-second time windows. Segment-level random-split screening was followed by Leave-One-Subject-Out (LOSO) cross-validation to obtain subject-independent performance estimates, and an ensemble approach combined predictions across K = 10 windows. The best single-window model achieved accuracy of 0.82 on the random split. Under LOSO, anterior regions generalised better across subjects: the frontal region achieved mean accuracy of 0.646 (AUC: 0.692). An ensemble of K = 10 windows further improved the prefrontal region to mean accuracy of 0.738. The occipital region, dominant in random-split screening, collapsed to near-chance under LOSO, revealing subject-specific EEG fingerprinting rather than genuine group differences. Bandpass filtering and z-score normalisation suffice for competitive EEG classification without manual feature extraction. Anterior cortical regions are identified as the most generalisable neural markers of hypnotic susceptibility.

cs.LG↗

arXiv2026

Region-Specific EEG Classification of Hypnotic Susceptibility Using a Sliding-Window 2D CNN: A Subject-Independent Evaluation

We investigate whether a 2D Convolutional Neural Network (CNN) trained on minimally preprocessed EEG signals can classify hypnotic susceptibility across distinct brain regions. Sixty-two male participants (32 Control, 30 Guided Imagery / Hipno group) underwent a 20-minute EEG recording using a 256-channel EGI cap. A sliding-window approach evaluates all 10-second windows within the recording for each of seven predefined brain regions (prefrontal, frontal, temporal, central, parietal, occipital, and facial), yielding up to 868 independently trained models. Initial screening with a segment-level 90/10 random split showed that five regions exceeded the 0.67 accuracy threshold, with the occipital region achieving the highest mean accuracy (0.721) across 90.3% of windows. Subject-independent evaluation via Leave-One-Subject-Out (LOSO) cross-validation, however, substantially revised this picture: occipital performance collapsed to near-chance (mean ACC 0.453, AUC 0.429), revealing that high random-split accuracy was driven by subject-specific EEG fingerprints rather than genuine group differences. In contrast, the frontal and prefrontal regions maintained above-chance LOSO accuracy (0.646 and 0.624, respectively), demonstrating genuine generalisability. These results underscore the necessity of subject-independent validation in EEG-based classification studies and highlight anterior cortical regions as more reliable neural markers of hypnotic susceptibility.

cs.LG↗

arXiv2026

CNN-based classifier for subject-independent MBTI Extraversion/Introversion preference classification from short-time EEG signals

This study examines whether short, minimally preprocessed EEG segments contain sufficient information to distinguish between individuals classified as extraverted or introverted according to the Myers--Briggs Type Indicator (MBTI). EEG recordings from 50 participants were divided into 4-s segments and analyzed using a subject-independent nested 5-fold cross-validation procedure to compare EEGNet, EEGProgress, and a newly proposed compact convolutional architecture, SSCNet. SSCNet achieved the highest mean accuracy (0.6481), AUC (0.7063), and F1-score (0.6276). The proposed architecture also showed the lowest variability across folds. SSCNet demonstrated the best computational efficiency, with the shortest median training epoch time and the highest training throughput despite having a similar number of trainable parameters to EEGProgress. The results suggest that MBTI Extraversion--Introversion preferences may be moderately predictable from short time-domain EEG segments without manual spectral feature extraction, while highlighting the importance of subject-independent validation and computationally efficient model design.

cs.LG↗

arXiv2024

Recurrent and Convolutional Neural Networks in Classification of EEG Signal for Guided Imagery and Mental Workload Detection

The Guided Imagery technique is reported to be used by therapists all over the world in order to increase the comfort of patients suffering from a variety of disorders from mental to oncology ones and proved to be successful in numerous of ways. Possible support for the therapists can be estimation of the time at which subject goes into deep relaxation. This paper presents the results of the investigations of a cohort of 26 students exposed to Guided Imagery relaxation technique and mental task workloads conducted with the use of dense array electroencephalographic amplifier. The research reported herein aimed at verification whether it is possible to detect differences between those two states and to classify them using deep learning methods and recurrent neural networks such as EEGNet, Long Short-Term Memory-based classifier, 1D Convolutional Neural Network and hybrid model of 1D Convolutional Neural Network and Long Short-Term Memory. The data processing pipeline was presented from the data acquisition, through the initial data cleaning, preprocessing and postprocessing. The classification was based on two datasets: one of them using 26 so-called cognitive electrodes and the other one using signal collected from 256 channels. So far there have not been such comparisons in the application being discussed. The classification results are presented by the validation metrics such as: accuracy, recall, precision, F1-score and loss for each case. It turned out that it is not necessary to collect signals from all electrodes as classification of the cognitive ones gives the results similar to those obtained for the full signal and extending input to 256 channels does not add much value. In Disscussion there were proposed an optimal classifier as well as some suggestions concerning the prospective development of the project.

cs.LG↗

arXiv2023

How are the people in the photos judged? Analysis of brain activity when assessing levels of trust and attractiveness

Trust is the foundation of every area of life. Without it, it is difficult to build lasting relationships. Unfortunately, in recent years, trust has been severely damaged by the spread of fake news and disinformation, which has become a serious social problem. In addition to trust, the factor influencing interpersonal relationships is perceived attractiveness, which is currently created to a large extent by digital media. Understanding the principles of judging others can be helpful in fighting prejudice and rebuilding trust in society. One way to learn about people's choices is to record their brain activity as they make choices. The article presents an experiment in which the faces of different people were presented, and the participants' task was to assess how much they can trust a given person and how attractive they are. During the study, the EEG signal was recorded, which was used to build models of logistic regression classifiers. In addition, the most active areas of the brain that participate in the assessment of trust and attractiveness of the face were indicated.

cs.HC↗

arXiv2016

Analytical modelling of temperature effects on synapses

It was previously reported, that temperature may significantly influence neural dynamics on different levels of brain modelling. Due to this fact, while creating the model in computational neuroscience we would like to make it scalable for wide-range of various brain temperatures. However currently, because of a lack of experimental data and an absence of analytical model describing temperature influence on synapses, it is not possible to include temperature effects on multi-neuron modelling level. In this paper, we propose first step to deal with this problem: new analytical model of AMPA-type synaptic conductance, which is able to include temperature effects in low-frequency stimulations. It was constructed on basis of Markov model description of AMPA receptor kinetics and few simplifications motivated both experimentally and from Monte Carlo simulation of synaptic transmission. The model may be used for efficient and accurate implementation of temperature effects on AMPA receptor conductance in large scale neural network simulations. This in fact, opens wide-range of new possibilities for researching an influence of temperature on brain functioning.

q-bio.NC↗