594 The serial position effect on ERPS recorded in a cued-recall task

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Abstracts

/International

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nest move. Additionally, the chess diagrams were degraded optically to 4 different degrees, using stochastic pixel modification of the presented diagrams, from undisturbed up to the borderline of recognizability. A multi-staged statistical analysis showed EC to be highly sensitive both to sensory (theta coherencesl and to mental activities (beta). Whereas theta coherences differed maximally (between degraded and undergraded presentations) at P02, differences in beta coherence depended upon the type of task: piece recognition affected occipital coherence, recognition of space relations additionally elicited right-temporal changes of coherence, and checkmate in the next move affected the prefrontal areas. The results obtained confirmed the high sensitivity of EC to subtle shifts of mental activity.

594 THE SERIAL POSITION RECORDED IN A CUED-RECALL

EFFECT TASK

ON

ERPS

Fabio Ferlazzo’ and Francesco Di Nocera’ i Department of Psychology, University of Cagliari, Italy, *Department of Psychology, University of Rome “La Sapienza”, Italy A large number of studies have recently investigated memory processes through Event Related Potential (ERPs), but the relationship between ERPs and memory is still largely undefined. Actually, the functional determinants of the larger positivity of the ERPs to previously presented stimuli (old/new effect) are still uncertain. We argue that this could be due to the use of recognition memory tasks whjch don’t allow to disentangle the different processes which can underly this effect (e.g. familiarity). In this experiment, we used a cued recall task suited to determine the effect of items’ serial positions on ERPs. Although other Authors have used cuedrecall in the form of stem-completion tasks (e.g., Allan and Rugg, 19971, none has investigated the serial position effect when cues are the initial and final letters of the words used as stimuli. Such effect would strongly support the hypothesis that familiarity is not a determinant of the old/new effect. In this study subjects were engaged in a cued-recall task along 9 blocks of trials. Each block included a study phase, when words were visually presented, and a test phase when cues (initial and final letters) to old words and to an equal number of new ones were displayed. During the test phase subjects were required to decide if the cue referred or not to an old word and to recall it. ERPs to correctly identified cues were averaged according to the study phase serial position. Results showed that differences between ERPs to cues to old and new words depend on the serial position of the stimuli, and that his effect emerges approximately 350 msec after the stimulus mset. These results support the hypothesis that the old/new :ffect does not depend on the familiarity of the stimuli and hat it may reflect a retrieval memory process. In order to waluate the robustness of these results we applied the boot-

of Psychophysiology300

(1998) 95-271

strap analysis (Efron and Tibshirani, 1993). This technique allowed us to divide our sample in “reliable subjects” and “unreliable subjects” and results suggest that individual differences should be carefully taken into account.

595 CLASSIFICATION OF P300 COMPONENT GLE TRIAL EVENT RELATED POTENTIALS ARTIFICIAL NEURAL NETWORK CLASSIFIER

IN SINUSING

Y.K. Yylmaz, T. Demiralp and H.G. Giil@ir* Electra-Neuro-Physiology Research and Application Center, University of Istanbul, * * Institute of Biomedical Engineering, Bogazici University In order to classify the P300 wave in single trials of an auditory oddball paradigm, an artificial neural network based on backpropagation error learning algorithm is implemented. After training, the neural network is expected to classify the responses into two categories according to the target and non-target stimulus types. To prevent overfitting, which is one of the most important weaknesses of the backpropagation, early stopping and lo-fold cross-validation are applied. The total data set is divided into 10 subsets. Eight of these are used for the training set. One subset is used for the validation and the remaining one is used for the test. All the possible combinations (90) of training, validation and test sets are considered. The neural network, after training with the original data set without any purification, can classify 72% of the responses *&rectly. The averages of the responses classified into the wrong categories by the network showed that the responses to the target stimuli, classified as non-target by the network contain no P300. In turn, the responses to the nontarget stimuli, classified as target by the network, contained the P300 wave. A simple data purification method, then, is suggested and applied to purify the data set before training the neural network. After purification, the neural network shows an improved performance of 96% correct classifications. The results show that a significant subset of target responses do not contain a P300 wave, whereas a subset of non-target responses do. This feature can be detected by applying a artificial neural network classi6er on single ERP sweeps and can be used as an additional parameter in the evaluation of P300 changes under normal or pathological conditions. This research is supported by TUBITAK (Turkish Scientific and Technical Research Council) project TBAG U 17-3.

596 MULTIPLE FUNCTIONAL COMPONENTS DURING ODDBALL P300: WAVELET TRANSFORM ANALYSIS OF SINGLE-SWEEP EVENT-RELATED BRAIN POTENTIALS Ahmet Ademoglu* , Juliana Yordanova* * , Vasil Kolev* * , Tamer Demiralp* * *