Parallel Factor Analysis can extract significant activities in multi channel EEG

Abstract

Human EEG is usually characterized and discussed mainly in frequency band, e.g. theta activity (4-8Hz), alpha activity (8-10Hz) and so on. Traditionally, band pass filter have been used for studying separated activities. However the peak and boundary of activities in frequency domain are different is subjects and trials. Accordingly numerical definition of boundary of activities is artificial and not appropriate, rather arbitrary. In this study we propose a new method for objectively extracting significant activities based on Parallel Factor Analysis (PARAFAC).PARAFAC is a general well-described decomposition method for dealing with multidimensional data and it has been used widely in food industry and chemometrics in general [1]. It is a generalization of Principal Components Analysis with the desirable advantage of having unique solution. In this work, PARAFAC was used for the first time in the analysis of EEG data in the space-time-frequency domain, in order to extract pure spectra and localization of separated brain activities.Appropriate EEG data for evaluating the performance of extracting activities by PARAFAC are those that contain more than two activities differing in space and frequency localization. For this purpose, EEG which was recorded during two different mental calculation tasks was analyzed. Task A is sequential subtraction 7 from 1000 without any trigger and task B is modified Uchida- Kreapelin test in which subject was required to sequentially add one digit number with ignoring tens place. The numbers were presented by artificial voice.EEG corresponding to task A, was wavelet transfromed and a three dimensional (channel-frequency-time) data set was obtained. PARAFAC successfully extracted theta and alpha activities in frequency and time domain(Fig.1). Estimated sources of extracted theta activity using Low Resolution Electromagnetic Tomography (LORETA) [2] were around anterior cingulate gyrus and medial frontal-orbital gyrus (Fig.2). For extracted alpha activity, the localization of estimated sources was occipital lobe. The comparison of these results with those obtained by ordinary band pass filter clearly shows the benefits of our new method as natural frequency separation.Second data, corresponding to task B, was event related type and transformed four dimensional data set (time-frequency-channel-trial). PARAFAC was applied to this data extracting also theta and alpha activities. Event related theta activity were located in similar region as task A, and alpha activities are decomposed into reasonable components whose time course characterized the event related appearance in each component. The pure spectrums for each extracted activity, showing corresponding frequency's peaks, were also found. The application of PARAFAC to three dimensional data (time-frequency-channel) and projecting significant activities on Talairach averaged brain by LORETA is also possible.These results demonstrated that PARAFAC is a new reliable method for the analysis of EEG in a variety of tasks.