demonstrated that corpus-extracted models of semantic knowledge can predict neural activation patterns recorded using fMRI . This could be a very powerful technique for evaluating conceptual models extracted from corpora ; however , fMRI is expensive and imposes strong constraints on data collection . Following on experiments that demonstrated that EEG activation patterns encode enough information to discriminate broad conceptual categories , we show that corpus-based semantic representations can predict EEG activation patterns with significant accuracy , and we evaluate the relative performance of different corpus-models on this task . Models of semantic relatedness induced from corpus data have proven effective in a number of empirical tasks ( Sahlgren , 2006 ) and there is increasing interest in whether distributional information extracted from corpora correlates with aspects of speakers ' semantic knowledge : see Lund and Burgess ( 1996 ) , Landauer and Dumais ( 1997 ) , Almuhareb ( 2006 ) , Padó and Lapata ( 2007 ) , Schulte i m Walde ( 2008 ) , among many others . For this purpose , corpus models have been tested on datasets that are based on semantic judgements ( metalinguistic or meta-cognitive intuitions about synonymy , semantic distance , category-membership ) or behavioural experiments ( semantic priming , property generation , free association ) . While all these data are valuable , they are indirect reflections of semantic knowledge , and when the predictions they make diverge from those of corpora , interpretation is problematic : is the corpus model missing essential aspects of semantics , or are non-semantic factors biasing the data elicited from informants ? Reading semantic processes and representations directly from the brain would be an ideal way to get around these limitations . Until recently , analysis of linguistic quantities using neural data collected with EEG ( measurement at the scalp of voltages induced by neuronal firing ) or fMRI ( measurement of changes of oxygen concentrations in the brain tied to cognitive processes ) had neither the advantages of corpora ( scale ) nor of informants ( finer grained judgements ) . However , some clear patterns of differential activity have been found for broad semantic classes . Viewing images of natural ( typically animals and plants ) and non-natural ( typically artefacts like tools or vehicles ) objects elicits different loci of activity in fMRI ( Martin and Chao , 2001 ) and EEG ( Kiefer , 2001 ) , that persist across participants . Differences have also been found in response to auditorily or visually presented words of different lexical classes , such as abstract / concrete , and verb / noun ( Pulvermüller , 2002 ) . But interpretation of such group results remains somewhat difficult , as they may be consistent with more than one distinction : the natural / artefactual division just mentioned , may rather be between living / nonliving entities , dynamic / static entities , or be based on embodied experience ( e.g. manipulable or not ) . More recently , however , machine learning and other numerical techniques have been successfully applied to extract semantic information from neural data in a more discriminative fashion , down to the level of individual concepts . The work presented here builds on two strands of previous work : Murphy et al . ( 2008 ) use EEG data to perform semantic categorisation on single stimuli ; and Mitchell et al . ( 2008 ) introduce an fMRIbased method that detects word level distinctions by learning associations between features of neural activity and semantic features derived from a corpus . We combine these innovations by introducing a method that extracts featural representations from the EEG signal , and uses corpusbased models to predict word level distinctions in patterns of EEG activity . The proposed method achieves a performance level significantly above chance ( also when distinguishing between concepts from the same semantic category , e.g. , dog and cat ) , and approaching that achieved with fMRI . The paper proceeds as follows . The next section describes a simple behavioural experiment where Italian-speaking participants had to name photographic images of mammals and tools while their EEG activity was being recorded , and continues to detail how the rich and multidimensional signals collected were reduced to a small set of optimally informative features using a new method . Section 3 describes a series of corpus-based semantic models derived from both a raw-text web corpus , and from various parsings of a conventional corpus . In Section 4 we describe the training of a series of linear models , that each learn the associations between a set of corpus semantic features and an individual EEG activity feature . By combining these models it is possible to predict the EEG activity pattern for a single unseen word , and compare this to the observed pattern for the corresponding concept . Results ( Section 5 ) show that these predictions succeed at a level significantly above chance , both for coarser distinctions between words in different superordinate categories ( e.g. , differentiating between drill and gorilla ) , and , at least for the model based on the larger web corpus , for those within the same category ( e.g. , drill vs spanner , koala vs gorilla ) . Our results show that corpus-extracted conceptual models can be used to distinguish between the EEG activation levels associated with conceptual categories to a degree that is significantly above chance . Though category specific patterns are detectable in the EEG signal alone ( as illustrated by the PCA analysis in figure 3 ) , on that basis we can not be sure that semantics is being detected . Some other property of the stimuli that co-varies with the semantic classes of interest could be responsible , such as visual complexity , conceptual familiarity , lexical frequency , or phonological form . Only by cross-training with individual corpus features and showing that these hold a predictive relationship to neural activity have we been able to establish that EEG patterns encode semantics . Present evidence indicates that fMRI may provide richer data for training such models than EEG ( Mitchell and colleagues obtain an average accuracy of 77 % , and 65 % for the within category setting ) . However , fMRI has several clear disadvantages as a tool for language researchers . First of all , the fine spatial resolution it provides ( down to 2 - 3 mm ) , while of great interest to neuroscientists , is not in itself linguistically informative . Its coarse temporal resolution ( of the order of several seconds ) , makes it ill-suited to analysing on-line linguistic processes . EEG on the other hand , despite its low spatial resolution ( several centimetres ) , gives millisecond-level temporal resolution , Finally , there is no prospect of fMRI being miniaturised , while wearable EEG systems are already becoming commercially available , making experimentation in more ecological settings a possibility ( e.g. , playing with a child , meeting at a desk , walking around ) . In short , while EEG can be used to carry out systematic investigations of categorical distinctions , doing so with fMRI would be prohibitively expensive . Present results indicate that distinctions between categories are easier than distinctions between category elements ; and that selecting the conceptual features by hand gives better results than discovering them automatically . Both of these results however may be due to limitations of the current method . One limitation is that we have been using the same set of features for all concepts , which is likely to blur the distinctions between members of a category more than those between categories . A second limitation of our present methodology is that it is constrained to use very small numbers of semantic features , which limits its applicability . For example it is hard to conceive of a small set of verbs , or other parts-ofspeech , whose co-occurrence patterns could successfully characterise the full range of meaning found in the human lexicon . Even the more economical corpus-extracted conceptual models tend to run in the hundreds of features ( Almuhareb , 2006 ) . We are currently working on variations in the method that will address these shortcomings . The web-based model with manually picked features outperformed all la Repubblica-based models . However , the results attained with repubblica-window-svd are encouraging , especially considering that we are reporting results for an EEG feature configuration optimised for the web data ( see footnote 5 ) , and that la Repubblica is several orders of magnitude smaller than the web . That data sparseness might be the main issue with la Repubblica models is suggested by the fact that repubblica-window-svd is the least sparse of them , since it does not filter data by position or dependency path , and compresses information from many verbs via SVD . In future research , we plan to extract richer models from larger corpora . And as the discriminative accuracy of crosstraining techniques improves , further insights into the relative validity of corpus representations will be attainable . One research aim is to see if individual corpus semantic properties are encoded neurally , so providing strong evidence for a particular model . These techniques may also prove more objective and reliable in evaluating representations of abstract concepts , for which it is more difficult to collect reliable judgements from informants .

Challenge: The expensive cost of using fMRI hinders studies on the relationship between corpus-extracted models of semantic knowledge and neural activation patterns.
Approach: They propose to use EEG activation patterns instead of fMRI to reduce the cost.
Outcome: They show that using EEG signals with corpus-based models, they can predict word level distinctions significantly above chance.