Popular Brain Computer Interfaces for Game Mechanics Control

Szajerman, Dominik; Warycha, Michał; Antonik, Arkadiusz; Wojciechowski, Adam

doi:10.1007/978-3-319-43982-2_11

Cited by 7 publications

(6 citation statements)

References 15 publications

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“…Thanks to the use of currently available portable devices this technique also can be applied for creating Brain Computer Interface (BCI) allowing remote interaction with applications. An example of the applicability of such systems for selected game interaction tasks was presented by [16]. A novel BCI circuit using the emotion fractal analysis method (EFAM) which quantifies emotions based on data obtained from an electroencephalogram (EEG) was develop by Kobayashi and Nakagawa to adjust the speed of an electric wheelchair in proportion to the intensity of the emotion [17].…”

Section: Blinking Extractionmentioning

confidence: 99%

Blinking Extraction in Eye gaze System for Stereoscopy Movies

et al. 2019

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The aim of this paper is to present methods for human eye blink recognition. The main function of blinking is to spread tears across the eye and remove irratants from the surface of the cornea and conjuctiva. Blinking can be associated with internal memory processing, fatigue or activation in central nervous system. There are currently many methods for automatic blink detection. The most reliable methods include EOG or EEG signals. These methods, however, are associated with a decrease in the comfort of the examined person. This paper presents a method to detect blinks with the eye-tracker device. There are currently many blink detection methods for this devices. Two popular eye-trackers were tested in this paper. In addition a method for improving detection efficiency was proposed.

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Section: Blinking Extractionmentioning

confidence: 99%

Blinking Extraction in Eye gaze System for Stereoscopy Movies

et al. 2019

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“…Modulation of the EEG activity can be used for discrete or continuous real-time control of the “Computer-User Interface” (van Gerven and Jensen, 2009; Treder et al , 2011; Szajerman et al , 2016). Visual BCIs often use intense visual stimulation by eye-trackers for simultaneously capturing the subjects’ focus of attention by detecting covert shifts of visual attention.…”

Section: Related Workmentioning

confidence: 99%

Joint analysis of simultaneous EEG and eye tracking data for video images

Szajerman

Napieralski

Lecointe

2018

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Purpose Technological innovation has made it possible to review how a film cues particular reactions on the part of the viewers. The purpose of this paper is to capture and interpret visual perception and attention by the simultaneous use of eye tracking and electroencephalography (EEG) technologies. Design/methodology/approach The authors have developed a method for joint analysis of EEG and eye tracking. To achieve this goal, an algorithm was implemented to capture and interpret visual perception and attention by the simultaneous use of eye tracking and EEG technologies. All parameters have been measured as a function of the relationship between the tested signals, which, in turn, allowed for a more accurate validation of hypotheses by appropriately selected calculations. Findings The results of this study revealed a coherence between EEG and eye tracking that are of particular relevance for human perception. Practical implications This paper endeavors both to capture and interpret visual perception and attention by the simultaneous use of eye tracking and EEG technologies. Eye tracking provides a powerful real-time measure of viewer region of interest. EEG technologies provides data regarding the viewer’s emotional states while watching the movie. Originality/value The approach in this paper is distinct from similar studies because it takes into account the integration of the eye tracking and EEG technologies. This paper provides a method for determining a fully functional video introspection system.

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“…Besides, it offers new communication channels or rehabilitation methods for patients with difficulty in body or language [2], [3] and provides more information output channels for healthy people. At present, there is a large body of research in many fields on BCI systems, e.g., sports rehabilitation [4], smart home [5], and entertainment [6].…”

Section: Introductionmentioning

confidence: 99%

Motor Imagery EEG Decoding Based on Multi-Scale Hybrid Networks and Feature Enhancement

Tang

Yang

Sun³

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Motor Imagery (MI) based on Electroencephalography (EEG), a typical Brain-Computer Interface (BCI) paradigm, can communicate with external devices according to the brain's intentions. Convolutional Neural Networks (CNN) are gradually used for EEG classification tasks and have achieved satisfactory performance. However, most CNN-based methods employ a single convolution mode and a convolution kernel size, which cannot extract multi-scale advanced temporal and spatial features efficiently. What's more, they hinder the further improvement of the classification accuracy of MI-EEG signals. This paper proposes a novel Multi-Scale Hybrid Convolutional Neural Network (MSHCNN) for MI-EEG signal decoding to improve classification performance. The two-dimensional convolution is used to extract temporal and spatial features of EEG signals and the one-dimensional convolution is used to extract advanced temporal features of EEG signals. In addition, a channel coding method is proposed to improve the expression capacity of the spatiotemporal characteristics of EEG signals. We evaluate the performance of the proposed method on the dataset collected in the laboratory and BCI competition IV 2b, 2a, and the average accuracy is at 96.87%, 85.25%, and 84.86%, respectively. Compared with other advanced methods, our proposed method achieves higher classification accuracy. Then we use the proposed method for an online experiment and design an intelligent artificial limb control system. The proposed method effectively extracts EEG sig-Manuscript

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Popular Brain Computer Interfaces for Game Mechanics Control

Cited by 7 publications

References 15 publications

Blinking Extraction in Eye gaze System for Stereoscopy Movies

Blinking Extraction in Eye gaze System for Stereoscopy Movies

Joint analysis of simultaneous EEG and eye tracking data for video images

Motor Imagery EEG Decoding Based on Multi-Scale Hybrid Networks and Feature Enhancement

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