EEGSpeech
importedsoftware/eegspeech
A brain-computer interface (BCI) for decoding speech phonemes from EEG signals using a hybrid CNN-LSTM model, with interactive Streamlit visualizations and Docker support.
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Software & Systems
- Subcategory
- unknown
- License
- MIT(osi)
- Status
- maintained
- Maturity
- deployed
- Organization
- unknown
- Country
- unknown
- Homepage
- unknown
- Repository
- github.com/tirth8205/EEGSpeech
- Documentation
- unknown
- Tags
- brain-computer-interface · cnn-lstm · deep-learning · docker · eeg · neuroscience · pytorch · signal-processing · speech-decoding · streamlit
- Regulatory
- unknown
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
- SYNAPTICONbrain-computer-interface · cnn-lstm · eeg
SYNAPTICON is a research prototype at the intersection of neuro-hacking, non-invasive BCIs, and foundational models, probing new territories of human expression, aesthetics, and AI alignment. At its…
- brainflowbrain-computer-interface · eeg · neuroscience · signal-processing
BrainFlow is a library intended to obtain, parse and analyze EEG, EMG, ECG and other kinds of data from biosensors
- mne-rtbrain-computer-interface · eeg · neuroscience · signal-processing
Real-time M/EEG signal processing
- torchsignalbrain-computer-interface · pytorch · signal-processing
Data manipulation and transformation for signal processing, powered by PyTorch
- HeartLens-AppleWatchcnn-lstm
Apple Watch ECG AI 深度分析 | 5 类心脏异常检测 + 心电波形染色可视化 + Qwen3.5 端侧解读 | 完全离线
- BciPybrain-computer-interface · eeg · signal-processing
Python Brain-Computer Interface Software
- api.github.com/repos/tirth8205/EEGSpeechretrieved 2026-08-25 · via github-api
Machine-imported from GitHub search. Last push 2025-07-16, 4 stars, license reported as MIT. Category and schematic were assigned by keyword heuristics and are unreviewed.
Not yet verified by a human. Correct this record →
/v1/entries/62.json→ .entries["eegspeech"]
Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.