Distinguishing Concept Categories from Single-Trial Electrophysiological Activity

Distinguishing Concept Categories from Single-Trial Electrophysiological Activity Brian Murphy (brian.murphy@unitn.it) Centre for Mind/Brain Sciences, Univ. of Trento, Italy Michele Dalponte (michele.dalponte@unitn.it) Dept. of Inf. Science and Engineering, Univ. Trento, Italy Massimo Poesio (massimo.poesio@unitn.it) Centre for Mind/Brain Sciences, Univ. of Trento, Italy / Dept. Of Computing and Electronic Systems, Univ. Essex, United Kingdom Lorenzo Bruzzone (lorenzo.bruzzone@unitn.it) Dept. of Inf. Science and Engineering, Univ. Trento, Italy temporal encoding of conceptual knowledge—e.g., for distinctions in the temporal domain between action verbs and nouns. To our knowledge, however, no previous study has attempted to identify regularities in conceptual knowledge activation on the basis of frequency information gathered through EEG. Yet if this is the case, and if similar categorical distinctions can be made with these techniques, our investigations of conceptual knowledge will greatly benefit, as the much lower overhead of EEG studies would make larger-scale investigations possible, particularly explorations of conceptual knowledge not directly concerned with issues of topography. The temporal resolution of EEG, relative to fMRI, also makes it possible to disentangle the effects of different stages of the process of perception and categorization. In this paper we report the results of just such a study. Spectral EEG data collected from healthy participants presented with visual and audio representations of concepts belonging to three categories—animals, tools, and plants— were classified using supervised machine learning methods developed for BCI applications (which are typically applied to lower level cognitive states such as imagined movements). To examine the “steady-state” conceptual representations that we assume are the end result of perceptual processes, only the period after stimulus offset was considered. We found that these methods are able to classify neural patterns with excellent accuracy—i.e., that enough information is encoded in frequency spectra to allow discrimination between conceptual categories. This paper is organized into six sections. After this introduction a background section presents the state-of-the- art and theoretical motivations. In the third section the experimental methodology is presented, and after that the main concepts of the data analysis are explained. Experimental results are presented in the fifth section. The last section is devoted to the discussion of the results. Abstract Differences in the neural representation of conceptual categories (such as buildings, tools, animals, plants, vehicles) are suggested by studies on brain-injury patients and by those using imaging techniques. Evidence that such conceptual distinctions are encoded spatially has been highlighted by fMRI techniques (Haxby et al, 2001), whereas ERP studies have identified differences in the temporal domain (Kiefer, 2001; Paz-Caballero et al., 2006). The study described here uses a machine learning technique (Dalponte et al., 2007), previously applied to Brain-Computer Interaction (BCI) tasks, to show that conceptual categories can be identified from single-trial spectral EEG responses to visually and auditorily presented stimuli in single participants. We found that using features extracted from frequency spectra, the categorial membership of a single stimulus presentation in one of three classes (animals, plants, tools) can be predicted with an average accuracy of 80%. Keywords: Conceptual Machine Learning, EEG. Categories, Semantic Spaces, Introduction Data about brain activity are providing exciting new insights on conceptual knowledge. Studies such as Martin et al (1996) provided evidence of topographical encoding of conceptual information through fMRI; machine learning techniques have been successfully used to classify brain activity recorded with such techniques into conceptual categories (Haxby et al 2001, Hanson et al 2004, Shinkareva et al 2008). All such studies rely on spatial information about neural patterns collected through fMRI. However, theories of conceptual memory based on the hypothesis that conceptual knowledge is distributed, taking the form of a ‘conceptual map’ (Spitzer, 1999) or ‘word web’ (Pulvermuller 2002), would predict a temporal or frequency encoding for conceptual information as well, involving some form of synchronization between the distinct brain areas activated in response to a concept. And indeed, several ERP studies reported by Pulvermuller (2002) found evidence for a