/Deep-BCI

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

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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

Website: 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).