Invariance between subjects of brain wave representations of language

Abstract

In three experiments, electric brain waves of 19 subjects were recorded under several different experimental conditions for two purposes. One was to test how well we could recognize which sentence, from a set of 24 or 48 sentences, was being processed in the cortex. The other was to study the invariance of brain waves between subjects. As in our earlier work, the analysis consisted of averaging over trials to create prototypes and test samples, to both of which Fourier transforms were applied, followed by filtering and an inverse transformation to the time domain. A least-squares criterion of fit between prototypes and test samples was used for classification. In all three experiments, averaging over subjects improved the recognition rates. The most significant finding was the following. When brain waves were averaged separately for two nonoverlapping groups of subjects, one for prototypes and the other for test samples, we were able to recognize correctly 90% of the brain waves generated by 48 different sentences about European geography.

Figure 1

Prototypes and test samples generated by two sentences. (Upper) The averaged, filtered brain waves for the sentence The capital of Italy is Paris. The solid curved line is the prototype, and the dotted line is the test sample. (Lower) The corresponding brain waves for the sentence The largest city of Austria is not Warsaw. Both halves were from the bipolar pair of sensors C4–T6. The x axis is measured in ms after the onset of the first word of the sentence.

Figure 2

Contour map of recognition-rate surface for bandpass filter parameters L and W for the separately averaged (SepAS) prototypes and test samples in experiment III. The x coordinate is the low frequency (L) in Hz and the y coordinate is the width (W) in Hz of a possible filter. The number plotted at a point on the map is the number of test samples, of 48 possible, correctly recognized with the Butterworth filter whose parameters are the coordinates of the point. The prediction at that point is that of the best bipolar pair of sensors, for that filter, so the surface shown optimizes the choice of bipolar pair for the parameters of a given filter. The single best prediction, 43 of 48, was made by the bipolar pair C4–T6.

Figure 3

Contour maps of recognition-rate surfaces for experiment II (Left) and experiment III (Right). Left shows 16 sensors of the 10–20 EEG system; Right shows 22 bipolar pairs of the 10–20 system. In both cases, the number plotted next to the location of a sensor (Left) or at the midpoint of the segment joining a bipolar pair (Right) is the number of test samples of sentences correctly recognized.