https://github.com/guangyizhangbci/eeg_riemannian
공식 코드Spatio-Temporal EEG Representation Learning on Riemannian Manifold and Euclidean Space
Papers with Code 아카이브
Electroencephalogram (EEG)
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
- 5
Electroencephalogram (EEG) is a method of recording brain activity using electrophysiological indexes. When the brain is active, a large number of postsynaptic potentials generated synchronously by neurons are formed after summation. It records the changes of electric waves during brain activity and is the overall reflection of the electrophysiological activities of brain nerve cells on the surface of cerebral cortex or scalp. Brain waves originate from the postsynaptic potential of the apical dendrites of pyramidal cells. The formation of synchronous rhythm of EEG is also related to the activity of nonspecific projection system of cortex and thalamus. EEG is the basic theoretical research of brain science. EEG monitoring is widely used in its clinical application.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/guangyizhangbci/parse
공식 코드PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognition
https://github.com/guangyizhangbci/pll-emotion-eeg
공식 코드https://github.com/guido151/Conditional-EEG-Diffusion
공식 코드Synthesizing EEG Signals from Event-Related Potential Paradigms with Conditional Diffusion Models
https://github.com/guoruilu/slimseiz
공식 코드SlimSeiz: Efficient Channel-Adaptive Seizure Prediction Using a Mamba-Enhanced Network
https://github.com/gzoumpourlis/ensemble-mi
공식 코드Motor Imagery Decoding Using Ensemble Curriculum Learning and Collaborative Training
https://github.com/hackerbci/eeg-with-jetsonnano
공식 코드Device JNEEG to convert Jetson Nano to brain-Computer interfaces. Short report
https://github.com/harshs27/tglad
공식 코드https://github.com/hdy6438/spec2volcamu-net
공식 코드https://github.com/hezy18/eeg_music
공식 코드Introducing EEG Analyses to Help Personal Music Preference Prediction
https://github.com/hgshrs/detector-atom-net
공식 코드https://github.com/hharveygit/CARLA_JNM
공식 코드CARLA: Adjusted common average referencing for cortico-cortical evoked potential data
https://github.com/hongmingli1995/cr-vae
공식 코드Causal Recurrent Variational Autoencoder for Medical Time Series Generation
https://github.com/hubertjb/dynamic-spatial-filtering
공식 코드Robust learning from corrupted EEG with dynamic spatial filtering
https://github.com/ibm/eeg-ieeg-brain-compressor
공식 코드https://github.com/ici-bci/eeg-disease-macs
공식 코드https://github.com/iis-eth-zurich/eeg-tcnet
공식 코드https://github.com/iiscleap/eegspeech-matchmismatch
공식 코드Enhancing the EEG Speech Match Mismatch Tasks With Word Boundaries
https://github.com/ilkyyldz95/eeg_mvts
공식 코드Unsupervised Multivariate Time-Series Transformers for Seizure Identification on EEG
https://github.com/iobt-vistec/min2net
공식 코드MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification
https://github.com/ironing/eeg-ad
공식 코드Task-oriented Self-supervised Learning for Anomaly Detection in Electroencephalography
https://github.com/ispamm/mhyeeg
공식 코드Hypercomplex Multimodal Emotion Recognition from EEG and Peripheral Physiological Signals
https://github.com/italoivo/Motorcoordination_Symbolic_Dynamics
공식 코드Symbolic dynamics of joint brain states during dyadic coordination
https://github.com/ivine-GIT/joint_CNN_LSTM_AAD
공식 코드Extracting the Auditory Attention in a Dual-Speaker Scenario from EEG using a Joint CNN-LSTM Model
https://github.com/izotov93/nneten
공식 코드https://github.com/ja-che/hidimstat
공식 코드Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso
https://github.com/jalilpour-m/match-mismatch-speech-features
공식 코드Extracting Different Levels of Speech Information from EEG Using an LSTM-Based Model
https://github.com/jason-qiu/eeg_language_alignment
공식 코드Can Brain Signals Reveal Inner Alignment with Human Languages?
https://github.com/jennyqsun/EEG-Decision-SincNet
공식 코드https://github.com/jesus-333/Dynamic-PyTorch-Net
공식 코드https://github.com/jesus-333/FBCSP-Python
공식 코드https://github.com/jiang-muyun/fast
공식 코드Decoding Covert Speech from EEG Using a Functional Areas Spatio-Temporal Transformer
https://github.com/jonaskohler/stereoeeg2speech
공식 코드Synthesizing Speech from Intracranial Depth Electrodes using an Encoder-Decoder Framework
https://github.com/joshparksj/eeg-wgan
공식 코드Improving EEG Signal Classification Accuracy Using Wasserstein Generative Adversarial Networks
https://github.com/julianagonzalezastudillo/netfeat
공식 코드Feature interpretability in BCIs: exploring the role of network lateralization
https://github.com/juriueno11/predann
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.