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