Using EEG recording, we train a classifier to predict the success of memory encoding of information. Currently, we are using a random selection of words from the English Language. Extensions of this experiment can involve phone numbers, pictures, sound, and other stimuli.
The inspiration for this project was from the abstract: Improving Memory Performance Using a Wearable BCI presented at the 2016 International BCI Society Meeting
Word list and word features was randomly generated from the Paivio et al. Word List Generator.
cd paradigm- Run
python encode.py - This will start the csv collection (watch out for hardcoded usb paths inside
encode.py) - This will start the Pygame presentation of the words while recording data in csv file in
datadirectory
cd paradigmpython recognize.py
More Info on Improving Memory Performance Using a Wearable BCI:
Corroborating previous studies [1,2,3], averaging over multiple EEG trials of the
encoding phase suggest that the power of the pre-stimulus theta and beta and the power of alpha after the onset of
the words over the parietal/occipital electrodes could be potentially useful as features for identifying poorly
versus well encoded words. Moreover, signal amplitudes from 1.5 to 2s after the onset of the words in the
parietal/occipital electrodes were significantly different between the two conditions
1 Schneider SL, Rose M. Intention to encode boosts memory-related pre-stimulus EEG beta power. NeuroImage. 125: 978-87, 2016.
2 Hanslmayr S, Staudigl T. How brain oscillations form memories-a processing based perspective on oscillatory subsequent memory effects. Neuroimage. 85:648-55, 2014.
3 Cohen N, Pell L, Edelson MG, Ben-Yakov A, Pine A, Dudai Y. Peri-encoding predictors of memory encoding and consolidation. Neuroscience & Biobehavioral Reviews. 50:128-42, 2015.