An open software package to develop BCI based brain and cognitive computing technology for recognizing user's intention using deep learning
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).
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. 3.0
Intelligent_BCI: contains a deep learning-based intelligent brain-computer interface-related function that enables high-performance intent recognition. 1.1 Atari_environment_sets_for_Goal_driven_learning 1.2 CNN_Based_Motor_Imagery_Intention_Classifier 1.2 EEG_Decoder_for_PE 1.3 Inter_Subject_Contrastive_Learning_for_EEG 1.4 Subject_Adaptive_EEG_based_Visual_Recognition
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 Channel Selection Method based on Relevance Score 2.1.2 Codes_for_Mobile_BCI_Dataset 2.1.3 Motor_imagery_on_treadmill 2.1.4 frequency_optimized_local_region_CSP 2.2 Intuitive_BCI: contains general brain-computer interface-related functions that enable high-performance intuitive BCI system 2.2.1 Attention-based_spatio-temporal-spectral_feature_learning_for_subject-specific_EEG_classification 2.2.2 Imagined Speech Classification 2.2.3 Phoneme-level Speech Classification 2.2.4 Speaker_Identification 2.2.5 Transfer Learning for Imagined Speech
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 Changes in Resting-state EEG by Working Memory Process 3.2 Detection_Micro-sleep_Using_Transfer_Learning 3.3 EEG_Feature_Fusion 3.4 EEG_ICA_Pipeline_Classifier_Comparison_Tool 3.5 Ear_EEG_Biosignal 3.6 Hybrid_EEG&NIRS_concatenate_CNN 3.7 Multi-modal_Awareness_Status_Monitoring 3.8 NIRS_Channel_Selection_Program 3.9 Prediction_Individual_Anesthetic_Sensitivity 3.10 Prediction_Long-term_Memory_Based_on_Deep_Learning 3.11 Sleep Classification For Sleep Inducing System 3.12 Sleep_Inertia_Analysis_Using_EEG_data 3.13 Sleep_Stage_Classification_Using_EEG
Zero-Training_BCI: contains zero-training brain-computer interface-related functions that enable to minimize additional training 4.1 MI_Analysis_based_on_ML 4.2 SSVEP_based_BCI_speller 4.3 SSVEP_based_Othello
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).
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. 2.0
Intelligent_BCI: contains a deep learning-based intelligent brain-computer interface-related function that enables high-performance intent recognition. 1.1 Atari_environment_sets_for_Goal_driven_learning 1.2 CNN_Based_Motor_Imagery_Intention_Classifier 1.3 Subject_Adaptive_EEG_based_Visual_Recognition
Ambulatory_BCI: contains general brain-computer interface-related functions that enable high-performance intent recognition in an ambulatory environment 2.1 Ambulatory_BCI 2.2 Intuitive_BCI
Cognitive_BCI': contains the cognitive state-related function that enables to estimate 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 Detection_Micro-sleep_Using_Transfer_Learning 3.2 Prediction_Individual_Anesthetic_Sensitivity 3.3 Prediction_Long-term_Memory_Based_on_Deep_Learning 3.4 Sleep_Stage_Classification_Using_EEG 3.5 EEG_Feature_Fusion 3.6 Ear_EEG_Biosignal 3.7 Hybrid_EEG&NIRS_concatenate_CNN 3.8 Multi-modal_Awareness_Status_Monitoring 3.9 NIRS_Channel_Selection_Program
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).