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

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.

  1. '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
  1. '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
  1. '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
  1. '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
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

    1. 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

    2. 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

    3. 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

    4. 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

    1. 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

    2. 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

    3. 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

    1. 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
deepbci
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