An open software package to develop BCI based brain and cognitive computing technology for recognizing user's intention using deep learning

Last update: Jan 08, 2023

Overview

Deep BCI SW ver. 1.0 is released.

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 forder and every function.

'Intelligent_BCI': contains deep learning-based intelligent brain-computer interface-related function that enables high-performance intent recognition.

Domain Adversarial NN for BCI: functions related to domaon adversarial neural networks
EEG based Meta RL Classifier: functions related to model-based reinforcement learning
GRU based Large Size EEG Classifier: data and functions relaated to gated recurrent unit
etc

'Ambulatory_BCI': contains general brain-computer interface-related functions that enable high-performance intent recognition in ambulatory environment

Channel Selection Method based on Relevance Score: functions related to electrode selection method by evaluating electrode's contribution to motor imagery based on relevance score and CNNs
Correlation optimized using rotation matrix: functions related to cognitive imagery analysis using correlation feature
SSVEP decoding in ambulatory envieonment using CNN: functions related to decoding scalp- and ear-EEG in ambulatory environment
etc

'Cognitive_BCI': contains cognitive state-related function that enable to estimaate the cognitive states from multi-modality and user-custermized BCI

multi-threshold graph metrics using a range of critiera: functions related to entrain brainwaves based on a combined auditory stimulus with a binaural beat
EEG_Authentication_Program: identifying individuals based on resting-state EEG
Ear_EEG_Drowsiness_Detection: identifying individuals based on resting-state EEG using convolutional neural network
etc

'Zero-Training_BCI': contains zero-training brain-computer interface-related functions that enable to minimize additional training

ERP-based_BCI_Algorithm_for_Zero_Training: functions related to Event Related Potential (ERP) analysis including feature extraction, classification, and visualization
SSVEP_based_Mind_Mole_Catching: functions allowing users to play mole cathcing game using their brain activity on single/two-player mode
SSVEP_based_BCI_speller: functions related to SSVEP-based speller containing nine classes
etc

Acknowledgement: This project was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning).

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Comments

No code about Inter-subject Contrastive Learning for SubjectAdaptive EEG-based Visual Recognition

Why have I not seen the code of this paper: Inter-subject Contrastive Learning for SubjectAdaptive EEG-based Visual Recognition?If it is convenient, could you please upload it again?

opened by SirWlt 1
question: datapath and filename

Hi, I'm checking on SSVEP code and confirming that the 'datapath' and 'filename' in SSVEP_CCA_Classification and SSVEP_FFT_Visualization are different. I think this part should be revised.

Thank you.
question

opened by MinjiLee-ku 1

Releases(Deep-BCI)

Deep-BCI(Dec 21, 2022)
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).
Source code(tar.gz)
Source code(zip)
Source.code.zip(1317.45 MB)
DeepBCI(Dec 28, 2021)
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

Zero-Training_BCI: contains zero-training brain-computer interface-related functions that enable to minimize additional training ERP-based_BCI_Algorithm_for_Zero_Training: functions related to Event-Related Potential (ERP) analysis including feature extraction, classification, and visualization 4.1 SSVEP_based_BCI_speller  4.2 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).
Source code(tar.gz)
Source code(zip)

Owner

deepbci

GitHub Repository

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