The importance of optimizing channel selection for portable brain-computer interface (BCI) technology is increasingly recognized. Effective channel selection reduces computational load and enhances user experience by ...
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The importance of optimizing channel selection for portable brain-computer interface (BCI) technology is increasingly recognized. Effective channel selection reduces computational load and enhances user experience by making BCI systems more comfortable and easier to use. A significant challenge lies in reducing the number of electrodes without compromising decoding accuracy. Although some methods have been proposed in previous studies, these often increase computational load and overlook the importance of channel selection across different subjects. Therefore, we propose a novel Multi-level Integrated EEG-Channel Selection method based on the Lateralization Index (MLI-ECS-LI). This method leverages the lateralization index in selecting important channels and can achieve the channel selection for the cross-tasks and the cross-subjects scenarios. To evaluate the effectiveness of the proposed method, the time and frequency domain features from selected channels were extracted. Three widely used classifiers, Least Squares Support Vector Machine (LSSVM), Random Forest (RF), and Support Vector Machine (SVM) were used to classify movement types based on these features. Compared to the conventional condition (C1-C6), the average decoding accuracies across 21 healthy subjects demonstrated an improved performance of 6.6%, 4.9%, 6.9% (LSSVM);3.8%, 2.8%, 4.5%(RF);and 7.6%, 5.6%, 9.2%(SVM) via using the channels selected from the conditions of the single task, the cross-tasks, and the cross-subjects scenarios, respectively. These results demonstrated the potential of the proposed method in improving the utility of the portable Motor Imagery brain-computerinterface (MI-BCI) and effectiveness in practical applications.
Both Augmented Reality (AR) and brain-computerinterfaces (BCI) have drawn a lot of attention in recent applications. These two new technologies will significantly impact and develop interactions between human and int...
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ISBN:
(纸本)9781728190488
Both Augmented Reality (AR) and brain-computerinterfaces (BCI) have drawn a lot of attention in recent applications. These two new technologies will significantly impact and develop interactions between human and intelligent agents. While there are several studies already conducted in the control of devices using AR based, steady state visually evoked potentials (SSVEP) control systems in a lab environment, this study seeks to implement a portable, closed-loop, AR-based BCI to assess the feasibility of controlling a physical device through SSVEP. This portable, closed-loop AR-based BCI provides users with the unique opportunity to simultaneously interact with the surrounding environment and control autonomous agents with an 88% accuracy. The potential benefits of this application include reduced restrictions on handicapped individuals or concurrent control of multiple devices through a single AR interface. Ultimately, we hope this outcome can bridge the BCI field with further real-world, practical applications.
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