EEG-Based Brain-Computer Interfacing (BCI)Dipali BansalRashima MahajanEEG-Based Brain-Computer Interfaces
For those interested in using our EEG signals for BCI development or machine learning research, we recommend using the preprocessed .pkl files available in Chisco. These files can be read using Python’s pickle package and are compatible with various versions of PyTorch. The code provided in “S...
In the quest to make Brain Computer Interfacing (BCI) more usable, dry electrodes have emerged that get rid of the initial 30 minutes required for placing an electrode cap. Another time consuming step is the required individualized adaptation to the BCI user, which invo...
1.1. EEG-Based BCI in Emotion Recognition Many studies suggest that emotional states are associated with electrical activity that is produced in the central nervous system. Brain activity can be detected through its electrical signals by sensing its variations, locations, and functional interactions...
Carofiglio V, Abbattista F (2013) A rough BCI-based assessment of user’s emotions for interface adaptation: Application to a 3D-virtual-environment exploration task. In: Ai* Hci@ Ai* Ia Causse M et al (2015) EEG/ERP as a measure of mental workload in a simple piloting task. Proc ...
Chisco: An EEG-based BCI dataset for decoding of imagined speech Open access21 November 2024 Background & Summary Brain-Computer Interfaces (BCIs) are a promising technology for improving the quality of life of people who have lost the capability to either communicate or interact with their enviro...
In EEG-based motor imagery Brain-Computer interface (BCI), EEG data collected in the calibration phase is used as a subject-specific model to classify the EEG data in the evaluation phase. Previous study has shown the feasibility of calibrating EEG-based BCI from passive movement. This paper ...
P300 Classification for EEG-based BCI system with Bayes LDA, SVM, LassoGLM and a Deep CNN methods - sajjadkarimi91/P300-BCI
A Brain Computer Interface (BCI) allows communication for impaired people unable to express their intention with common channels. Electroencephalography (EEG) represents an effective tool to allow the implementation of a BCI. The present paper describes a modular framework for the implementation of the...
One such application is electroencephalogram (EEG) based brain-computer interface (BCI), where multiple convolutional neural network (CNN) models have been proposed for EEG classification. However, it has been found that deep learning models can be easily fooled with adversarial examples, which are ...