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Search results for: BRAIN-COMPUTER INTERFACE

Best results in : Research Potential Pokaż wszystkie wyniki (106)

Search results for: BRAIN-COMPUTER INTERFACE

  • Zespół Systemów Multimedialnych

    Research Potential

    * technologie archiwizacji, rekonstrukcji i dostępu do nagrań archiwalnych * technologie inteligentnego monitoringu wizyjnego i akustycznego * multimedialne technologie telemedyczne * multimodalne interfejsy komputerowe

  • Zespół Systemów Multimedialnych

    Research Potential

    * technologie archiwizacji, rekonstrukcji i dostępu do nagrań archiwalnych * technologie inteligentnego monitoringu wizyjnego i akustycznego * multimedialne technologie telemedyczne * multimodalne interfejsy komputerowe

  • Zespół Inżynierii Biomedycznej

    Research Potential

    Inżynieria biomedyczna stanowi nową interdyscyplinarną dziedzinę wiedzy zlokalizowaną na pograniczu nauk technicznych, medycznych i biologicznych. Według opinii WHO (World Health Organization) można ją zaliczyć do głównych (obok inżynierii genetycznej) czynników decydujących o postępie współczesnej medycyny. Rosnące znaczenie kształcenia w zakresie INŻYNIERII BIOMEDYCZNEJ wynika z faktu, że specjaliści tej dyscypliny są potrzebni...

Best results in : Business Offer Pokaż wszystkie wyniki (35)

Search results for: BRAIN-COMPUTER INTERFACE

Other results Pokaż wszystkie wyniki (3780)

Search results for: BRAIN-COMPUTER INTERFACE

  • STEADY STATE VISUALLY EVOKED POTENTIALS FOR BRAIN COMPUTER INTERFACE

    Publication

    - Year 2013

    An experiment conducted to validate a possibility of use a single active electrode EEG device for detecting Steady State Visually Evoked Potentials (SSVEP) is shown. A LED stimulator was applied to stimulate patients with two different frequencies - 13 Hz and 17 Hz. First, EEG signals were recorded and pre-processed using MATLAB software. In the next step recordings were analysed and classified employing the WEKA software. As indicated...

  • Deep learning approach on surface EEG based Brain Computer Interface

    Publication

    - Year 2022

    In this work we analysed the application of con-volutional neural networks in motor imagery classification for the Brain Computer Interface (BCI) purposes. To increase the accuracy of classification we proposed the solution that combines the Common Spatial Pattern (CSP) with convolutional network (ConvNet). The electroencephalography (EEG) is one of the modalities we try to use for controlling the prosthetic arm. Therefor in this...

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  • Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning

    Publication

    - SENSORS - Year 2020

    The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was...

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  • Analysis of the Capability of Deep Learning Algorithms for EEG-based Brain-Computer Interface Implementation

    Publication

    - Year 2023

    Machine learning models have received significant attention for their exceptional performance in classifying electroencephalography (EEG) data. They have proven to be highly effective in extracting intricate patterns and features from the raw signal data, thereby contributing to their success in EEG classification tasks. In this study, we explore the possibilities of utilizing contemporary machine learning algorithms in decoding...

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  • Brain-Computer Interfaces

    Journals

    ISSN: 2326-263X , eISSN: 2326-2621