Lois M. Black

Computational prosodic markers for autism

We present results obtained with new instrumental methods for the acoustic analysis of prosody to evaluate prosody production by children with Autism Spectrum Disorder (ASD) and Typical Development (TD). Two tasks elicit focal stress - one in a vocal imitation paradigm, the other in a picture-description paradigm; a third task also uses a vocal imitation paradigm, and requires repeating stress patterns of two-syllable nonsense words. The instrumental methods differentiated significantly between the ASD and TD groups in all but the focal stress imitation task. The methods also showed smaller differences in the two vocal imitation tasks than in the picture-description task, as was predicted. In fact, in the nonsense word stress repetition task, the instrumental methods showed better performance for the ASD group. The methods also revealed that the acoustic features that predict auditory-perceptual judgment are not the same as those that differentiate between groups. Specifically, a key difference between the groups appears to be a difference in the balance between the various prosodic cues, such as pitch, amplitude, and duration, and not necessarily a difference in the strength or clarity with which prosodic contrasts are expressed.

A comparison of different dimensionality reduction and feature selection methods for single trial ERP detection

Dimensionality reduction and feature selection is an important aspect of electroencephalography based event related potential detection systems such as brain computer interfaces. In our study, a predefined sequence of letters was presented to subjects in a Rapid Serial Visual Presentation (RSVP) paradigm. EEG data were collected and analyzed offline. A linear discriminant analysis (LDA) classifier was designed as the ERP (Event Related Potential) detector for its simplicity. Different dimensionality reduction and feature selection methods were applied and compared in a greedy wrapper framework. Experimental results showed that PCA with the first 10 principal components for each channel performed best and could be used in both online and offline systems.