Robust estimation of event-related potentials via particle filter.

BACKGROUND AND OBJECTIVE: In clinical examinations and brain-computer interface (BCI) research, a short electroencephalogram (EEG) measurement time is ideal. The use of event-related potentials (ERPs) relies on both estimation accuracy and processing time. We tested a particle filter that uses a large number of particles to construct a probability distribution. METHODS: We constructed a simple model for recording EEG comprising three components: ERPs approximated via a trend model, background waves constructed via an autoregressive model, and noise. We evaluated the performance of the particle filter based on mean squared error (MSE), P300 peak amplitude, and latency. We then compared our filter with the Kalman filter and a conventional simple averaging method. To confirm the efficacy of the filter, we used it to estimate ERP elicited by a P300 BCI speller. RESULTS: A 400-particle filter produced the best MSE. We found that the merit of the filter increased when the original waveform already had a low signal-to-noise ratio (SNR) (i.e., the power ratio between ERP and background EEG). We calculated the amount of averaging necessary after applying a particle filter that produced a result equivalent to that associated with conventional averaging, and determined that the particle filter yielded a maximum 42.8% reduction in measurement time. The particle filter performed better than both the Kalman filter and conventional averaging for a low SNR in terms of both MSE and P300 peak amplitude and latency. For EEG data produced by the P300 speller, we were able to use our filter to obtain ERP waveforms that were stable compared with averages produced by a conventional averaging method, irrespective of the amount of averaging. CONCLUSIONS: We confirmed that particle filters are efficacious in reducing the measurement time required during simulations with a low SNR. Additionally, particle filters can perform robust ERP estimation for EEG data produced via a P300 speller.

Preliminary study for extraction of P300 and SSVEP by stimulus presentation using phase inversion technique in hybrid BCI.

In this study, we propose a novel stimulation presentation method for the hybrid BCI of the P300 and steady state visual evoked potential (SSVEP) to separate the two components efficiently. The method produces the separation by generating the P300 at two time points whose phase difference is π radians in the SSVEP component corresponding to stimulus frequency. Assuming that the consecutive two P300 responses are identical and the SSVEP is sinusoidal, the P300 can be extracted as a summation of the above two responses by suppressing the SSVEP. Also, the SSVEP can be detected by the subtraction of the above two responses. Accordingly, this method is realized by a stimulus pair consisting of the above two stimuli. In an EEG experiment, we used a checkerboard stimulus and character presentation for obtaining the SSVEP and P300, respectively. The stimulus frequencies of the checkerboard were assigned to 5 Hz and 3 Hz to classify the target character from the two given characters. The results showed the appearance of a prominent P300 component from only one pair of stimuli, even though the fundamental and harmonic frequency components of the SSVEP for lower stimulus frequencies are not very stable. This is because of the asymmetry of the positive and negative potentials for the SSVEP. It is a good idea to use a stimulus frequency that overlaps with the P300 frequency band, because this method does not separate the P300 and SSVEP by EEG frequency difference. Moreover, it reduces the measurement time (i.e., it shortens the number of averagings required for P300 estimation) because the SSVEP cancels out if it is sinusoidal. We consider that this will be a useful method to estimate the P300 and SSVEP simultaneously from these aspects.

Quantitative evaluation of photic driving response for computer-aided diagnosis.

The aim of our research is the quantification of the photic driving response, a routine electroencephalogram (EEG) examination, for computer-aided diagnosis. It is well known that the EEG responds not only to the fundamental frequency but also to all sub and higher harmonics of a stimulus. In this study, we propose a method for detecting and evaluating responses in screening data for individuals. This method consists of two comparisons based on statistical tests. One is an intraindividual comparison between the EEG at rest and the photic stimulation (PS) response reflecting enhancement and suppression by PS, and the other is a comparison between data from an individual and a distribution of normals reflecting the position of the individual's data in the distribution of normals in the normal database. These tests were evaluated using the Z-value based on the Mann-Whitney U-test. We measured EEGs from 130 normal subjects and 30 patients with any of schizophrenia, dementia and epilepsy. Normal data were divided into two groups, the first consisting of 100 data for database construction and the second of 30 data for test data. Using our method, a prominent statistical peak of the Z-value was recognized even if the harmonics and alpha band overlapped. Moreover, we found a statistical difference between patients and the normal database at diagnostically helpful frequencies such as subharmonics, the fundamental wave, higher harmonics and the alpha frequency band.

Estimation of temporary change of activation areas by moving an analysis time window in fMRI measurement.

In this paper, we propose a method to acquire temporal changes of activations by moving an analysis time window. An advantage of this method is that it can acquire rough changes of activated areas even with the data having low time resolution. We ascertained that activations from our method do not contradict previous reports on the oddball paradigm, thus showing its effectiveness. Eight normal subjects participated in the study, which consisted of a random series of 30 target and 70 nontarget stimuli. We investigated the activated area in three kinds of analysis time sections, from stimulus onset to 5 s after the stimulus (time section A), from 2 to 7 s after (B) and from 4 to 9 s after (C). In time section A, representative activated areas were regions including the left and supplementary motor areas (SMA), and cerebellum. In B, regions including the left motor area and SMA, right parahippocampal gyrus (Broadmann Area (BA) 30), right limbic lobe and cerebellum were activated. In C, bilaterally postcentral gyrus (BA 3,40), right anterior cingulate (ACC, BA 32), left middle frontal gyrus (BA 9) and right parahippocampal gyrus were activated. Most activations were consistent with previous studies.