Decoding working memory-related information from repeated psychophysiological EEG experiments using convolutional and contrastive neural networks

Objective. Extracting reliable information from electroencephalogram (EEG) is difficult because the low signal-to-noise ratio and significant intersubject variability seriously hinder statistical analyses. However, recent advances in explainable machine learning open a new strategy to address this problem. Approach. The current study evaluates this approach using results from the classification and decoding of electrical brain activity associated with information retention. We designed four neural network models differing in architecture, training strategies, and input representation to classify single experimental trials of a working memory task. Main results. Our best models achieved an accuracy (ACC) of 65.29 ± 0.76 and Matthews correlation coefficient of 0.288 ± 0.018, outperforming the reference model trained on the same data. The highest correlation between classification score and behavioral performance was 0.36 (p = 0.0007). Using analysis of input perturbation, we estimated the importance of EEG channels and frequency bands in the task at hand. The set of essential features identified for each network varies. We identified a subset of features common to all models that identified brain regions and frequency bands consistent with current neurophysiological knowledge of the processes critical to attention and working memory. Finally, we proposed sanity checks to examine further the robustness of each model's set of features. Significance. Our results indicate that explainable deep learning is a powerful tool for decoding information from EEG signals. It is crucial to train and analyze a range of models to identify stable and reliable features. Our results highlight the need for explainable modeling as the model with the highest ACC appeared to use residual artifactual activity

Neurofeedback method – what might influence on therapy BFB results

This paper presents basic principles biofeedback method, particularly neurofeedback training method and shown influence FFT window on the results BFB therapy. Is described a general principle operation method and mechanisms used for biofeedback therapy. On the basis of analysis are set session parameters, which are designed to promote a range of brain waves, and the inhibition of another range of brain waves

Application of composite materials in underground mining industry – fore-shaft closing platform

According to Polish law regulations, fore-shaft in underground mines in process of its liquidation must be either filled with bulk material or closed with double-deck platform on its top. As liquidation by closing is cheaper and easier than filling, steel closing platforms are typically used for this purpose. However, steel price fluctuations due to COVID-19 pandemic together with rapid development of composite materials, make application of composite structure a tempting direction. The article presents a design of composite double-deck closing platform for fore-shaft liquidation in one of the collieries located in the eastern area of Silesian Coal Basin. Presented solution was thoroughly calculated and tested and then assembled in the mine. The aim of the research was to prove applicability of composite structures in underground mines with maintaining proper level of safety