Wyniki wyszukiwania - Biblioteka Nauki

1

Analysis of the application of brain-computer interfaces of a selected paradigm in everyday life

100%

Mróz K. , Plechawska-Wójcik M.

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tom Vol. 23

118--122

EN

The main objective of this paper is to carry out a research on the analysis of the use of brain-computer interface in everyday life. The article presents the method of recording brain activity, electroencephalography, which was used in the study. The brain activity used in the brain-computer interface and the general principle of brain-computer interface design are also described. The performed study allowed to develop an analysis of the obtained results in the matter of evaluating the usability of brain-computer interfaces using motor imagery. As a result of the process of analyzing the results obtained during the research, it was found that each subsequent experiment allowed for obtaining more favourable results than the previous one. The reason for this was the use of an additional training session for the next test person. In the final stage, it was possible to evaluate the usability of the brain-computer interface in everyday life

PL

Głównym celem artykułu jest przeprowadzenie badania nad analizą wykorzystania interfejsu mózg-komputer w życiu codziennym. W artykule przedstawiono metodę rejestrowania aktywności mózgu, elektroencefalografię, która została wykorzystana w badaniu. Opisano również aktywność mózgu wykorzystywaną w interfejsie mózg-komputer oraz ogólną zasadę projektowania interfejsu mózg-komputer. Przeprowadzone badanie pozwoliło na opracowanie analizy uzyskanych wyników w zakresie oceny użyteczności interfejsów mózg-komputer z wykorzystaniem obrazowania motorycznego. W wyniku procesu analizy wyników uzyskanych podczas przeprowadzania badań ustalono, iż każdy następnie zrealizowany eksperyment pozwalał na uzyskanie korzystniejszych wyników od poprzedniego. Powodem tego było zastosowanie dodatkowej sesji treningowej dla kolejnych badanych osób. W końcowym etapie można było ocenić przydatność interfejsu mózg-komputer w życiu codziennym

2

Classic genetic algorithm vs. genetic algorithm with aggressive mutation for feature selection for a brain-computer interface

84%

Rejer I. , Lorenz K.

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tom R. 91, nr 2

98-102

EN

The classic genetic algorithm has been successfully applied to many optimization problems. However, its usefulness is limited when it comes to feature selection, particularly if a high reduction rate is expected. The algorithm, in its classic version, returns feature sets containing approximately 50% of the total number of features. In order to decrease this rate, a penalty term penalizing individuals of too many features is often added to the fitness function. This solution seems to be reasonable but, as will be shown in this paper, provides only a slight improvement in the reduction rate. In order to obtain a satisfactory classification accuracy and a high reduction rate, not only the fitness function but also other algorithm elements must be reconsidered.

PL

Klasyczny algorytm genetyczny był z powodzeniem stosowany w wielu problemach optymalizacyjnych, jednakże jego użyteczność jest ograniczona w problemach selekcji cech, zwłaszcza jeżeli wymagana jest wysoka stopa redukcji cech. Algorytm, w jego klasycznej wersji, zwraca zbiory cech zawierające około 50% pierwotnej liczby cech. W celu zmniejszenia tej liczby, do funkcji przystosowania algorytmu dołącza się często człon kary, karzący osobniki kodujące zbiory o zbyt dużej liczbie cech. Takie rozwiązanie wydaje się być rozsądne, ale, jak zostanie to przedstawione w artykule pozwala jedynie na niewielką poprawę stopy redukcji. Stąd, w celu uzyskania satysfakcjonującej dokładności klasyfikacji i wysokiej stopy redukcji, nie tylko funkcja przystosowania, ale również inne elementy algorytmu muszą zostać wzięte pod uwagę.

3

The quantitative application of channel importance in movement intention decoding

84%

Wang L. , Li M.

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tom Vol. 42, no. 2

630--645

EN

The complex brain network consists of multiple collaborative regions, which can be activated to varying degrees by motor imagery (MI) and the induced electroencephalogram (EEG) recorded by an array of scalp electrodes is usually decoded for driving rehabilitation system. Either all channels or partially selected channels are equally applied to recognize movement intention, which may be incompatible with the individual differences of channels from different locations. In this paper, a channel importance based imaging method is proposed, denoted as CIBI. For each electrode of MI-EEG, the power over 8–30 Hz band is calculated from discrete Fourier spectrum and input to random forest algorithm (RF) to quantify its contribution, namely channel importance (CI); Then, CI is used for weighting the powers of α and β rhythms, which are interpolated to a 32 x 32 grid by using Clough-Tocher method respectively, generating two main band images with time-frequency-space information. In addition, a dual branch fusion convolutional neural network (DBFCNN) is developed to match with the characteristic of two MI images, realizing the extraction, fusion and classification of comprehensive features. Extensive experiments are conducted based on two public datasets with four classes of MI-EEG, the relatively higher average accuracies are obtained, and the improvements achieve 23:95% and 25:14% respectively when using channel importance, their statistical analysis are also performed by Kappa value, confusion matrix and receiver operating characteristic. Experiment results show that the personalized channel importance is helpful to enhance inter-class separability as well as the proposed method has the outstanding decoding ability for multiple MI tasks.

4

Compact convolutional neural network (CNN) based on SincNet for end-to-end motor imagery decoding and analysis

67%

Izzuddin T. A. , Safri N. M. , Othman M. A.

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2021

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tom Vol. 41, no. 4

1629--1645

EN

In the field of human-computer interaction, the detection, extraction and classification of the electroencephalogram (EEG) spectral and spatial features are crucial towards developing a practical and robust non-invasive EEG-based brain-computer interface. Recently, due to the popularity of end-to-end deep learning, the applicability of algorithms such as convolutional neural networks (CNN) has been explored to achieve the mentioned tasks. This paper presents an improved and compact CNN algorithm for motor imagery decoding based on the adaptation of SincNet, which was initially developed for speaker recognition task from the raw audio input. Such adaptation allows for a compact end-to-end neural network with state-of-the-art (SOTA) performances and enables network interpretability for neurophysiological validation in cortical rhythms and spatial analysis. In order to validate the performance of proposed algorithms, two datasets were used; the first is the publicly available BCI Competition IV dataset 2a, which was often used as a benchmark in validating motor imagery classification algorithms, and the second is a dataset consists of primary data initially collected to study the difference between motor imagery and mental-task associated motor imagery BCI and was used to test the plausibility of the proposed algorithm in highlighting the differences in terms of cortical rhythms. Competitive decoding performance was achieved in both datasets in comparisons with SOTA CNN models, albeit with the lowest number of trainable parameters. In addition, it was shown that the proposed architecture performs a cleaner band-pass, highlighting the necessary frequency bands that were crucial and neurophysiologically plausible in solving the classification tasks.