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Deep BCI SW ver. 4.0

19 Feb 02:46
@deep-bci-g deep-bci-g
DeepBCI-SW
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Deep BCI SW ver. 4.0 Latest
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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).

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Deep BCI SW ver. 3.0

21 Dec 01:29
@jiwonLee-KU jiwonLee-KU
Deep-BCI
a0fd9a3
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Deep BCI SW ver. 3.0

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).

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Deep BCI SW ver. 2.0

28 Dec 02:25
@YoungseokKweon-ku YoungseokKweon-ku
DeepBCI
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Deep BCI SW ver. 2.0

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).

Assets 2
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