An open software package to develop Brain-Computer Interface (BCI) based brain and cognitive computing technology for recognizing user's intention using deep learning
Web site: http://deepbci.korea.ac.kr/
We provide detailed information in each folder and every function.
The following items were updated in Deep BCI SW ver. 4.0
Intelligent_BCI: contains a deep learning-based intelligent brain-computer interface-related function that enables high-performance intent recognition.
1.1 Source_Free_Subject_Adaptation_for_EEG
Ambulatory_BCI & Intuitive_BCI
2.1 Ambulatory_BCI: contains general brain-computer interface-related functions that enable high-performance intent recognition in an ambulatory environment
2.1.1 Motor_imagery_on_treadmill
2.2 Intuitive_BCI: contains general brain-computer interface-related functions that enable high-performance intuitive BCI system
2.2.1 Imagined Speech Classification
2.2.2 Phoneme-level Speech Classification
2.2.3 Speaker_Identification
Cognitive_BCI: contains the cognitive state-related function that enables to estimate of the cognitive states from multi-modality and user-customized BCI multi-threshold graph metrics using a range of criteria: functions related to entrain brainwaves based on a combined auditory stimulus with a binaural beat
3.1 EEG_Feature_Fusion
3.2 Self-supervised Learning for Sleep Stage Classification
3.3 Sleep_Inertia_Analysis_Using_EEG_data
Zero-Training_BCI: contains zero-training brain-computer interface-related functions that enable to minimize additional training
Acknowledgment: This project was supported by the Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korean government (No. 2017-0-00451, Development of BCI-based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning).