We study the use of data collected via electroencephalography (EEG) to classify stimuli presented to
subjects using a variety of mathematical approaches. We report an experiment with three objectives:
1) To train individual classifiers that reliably infer the class labels of visual stimuli using EEG data
collected from subjects; 2) To demonstrate brainsourcing, a technique to combine brain responses
from a group of human contributors each performing a recognition task to determine classes of
stimuli; 3) To explore collaborative filtering techniques applied to data produced by individual
classifiers to predict subject responses for stimuli in which data is unavailable or otherwise missing.
We reveal that all individual classifier models perform better than a random baseline, while a
brainsourcing model using data from as few as four participants achieves performance superior
to any individual classifier. We also show that matrix factorization applied to classifier outputs
as a collaborative filtering approach achieves predictive results that perform better than random.
Although the technique is fairly sensitive to the sparsity of the dataset, it nonetheless demonstrates
a viable proof-of-concept and warrants further investigation.
