Learn to control your brain: Brain Computer Interfacing with Python
summary
Shows a working neurofeedback pipeline that captures EEG over Lab Streaming Layer, filters in Python and returns real‑time audio/visual feedback; offline analysis uses MNE-Python and real‑time code runs in Jupyter with NFBLab and PsychoPy. Deployments hitable in Brisbane and Newcastle using affordable OpenBCI amps. Training runs ~20 sessions over weeks, thresholds yield ~10–15% initial rewards, and the system demonstrably streams, decodes and updates stimuli in real time.
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chapters (20)
- 0:00Learn to control your brain Brain Computer Interfacing with Python
- 1:42Brain Signals
- 2:25The EEG signal
- 4:30EEG Rhythms
- 6:23Python Library for EEG analysis
- 6:46Signals & Quality
- 8:59Signals in Real-Time
- 9:09I. Real-Time Data Stream
- 10:41Brain / Machine Interfacing
- 11:53Neurofeedback Learning
- 12:38Learning Conditioning
- 13:32Neurofeedback Training
- 14:04Repetition
- 15:16Scan during EEG Neurofeedback
- 15:32Promote BCI research
- 17:03Deploy NF Software
- 21:13Concurrency and Trollius
- 22:11Python 2.7 - 3 and Asyncore
- 22:26Real-Time Analysis (nfertime) Python 3.8
- 24:18Repositories
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