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Neurotech22-Lecture-3

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📗 -> Week 3: The BCI Pipeline

Lecture 3

🎤 Vocab

❗ Information

OpenBCI:

  • A BCI that records EEG data from the scalp
  • EEG (analog signal) -> OpenBCI (digital signal)

The Pipeline

The Brain -

Produces the electrical signals internally, that is received from the scalp by the EEG

Scalp Signals -> Signal Acquisition

Signal Processing Hardware -

Obtaining the signal is a crucial step, and OpenBCI is a popular candidate

  • Open source
  • High compatibility
    Sampling frequency - Rate at which the EEG captures / polls the data

OpenBCI (digital signal) -> Preprocessing (digital signal to be read by the computer)

Preprocessing -

Brainflow / MNE

  • Both are highly advanced python libraries designed to take, read, and process brain activity data
    Preprocessing mainly consists of filtering the data such that it is in a usable state to feed into the next step of the process:
  • Bandpass filter - Removes all the high and low frequencies and keeps a band of them in the middle
  • High Pass - Gets rid of high freqs
  • Low Pass - Gets rid of low freqs
    Preprocessing occurs in both hardware and software
  • Hardware filters are significantly faster than digital filters
    Fourier Transform Vid

Preprocessing (digital EEG signal) -> Processing (internal python data structures)

Algorithm / Processing

Brainflow / MNE

  • The algorithm/processing stage processes the data to find what we want
  • This can involve creating a classifier

Processing (internal python data structures) -> Decision

What are classifiers?

A classifier is a model that is able to discriminate between different types of inputs. In our case we want out BCI to be able differentiate between different types of signals and artifacts.

Decision

The stage of the pipeline where the BCI makes a judgement regarding its data

  • BCI looking for changes in brain activity based on two different frequencies and rendered a binary decision based on that
    After this, the pipeline will be restarted

✒️ -> Scratch Notes

Neurotech Resource Sheet

🧪-> Example

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