Browsing by Author "Pereira, A.T."
Now showing 1 - 2 of 2
Results Per Page
Sort Options
- ERP correlates of error processing during performance on the HalsteadCategory TestPublication . Santos, I.M.; Teixeira, Ana; Tomé, A.M.; Pereira, A.T.; Rodrigues, P.; Vagos, P.; Costa, J.; Carrito, M.L.; Oliveira, B.; DeFilippis, N.A.; Silva, C.F.The Halstead Category Test (HCT) is a neuropsychological test that measures a person's ability to formulate and apply abstract principles. Performance must be adjusted based on feedback after each trial and errors are common until the underlying rules are discovered. Event-related potential (ERP) studies associated with the HCT are lacking. This paper demonstrates the use of amethodology inspired on Singular SpectrumAnalysis (SSA) applied to EEG signals, to remove high amplitude ocular andmovement artifacts during performance on the test. This filtering technique introduces no phase or latency distortions, with minimum loss of relevant EEG information. Importantly, the test was applied in its original clinical format, without introducing adaptations to ERP recordings. After signal treatment, the feedback-related negativity (FRN) wave, which is related to error-processing, was identified. This component peaked around 250ms, after feedback, in fronto-central electrodes. As expected, errors elicited more negative amplitudes than correct responses. Results are discussed in terms of the increased clinical potential that coupling ERP informationwith behavioral performance data can bring to the specificity of theHCT in diagnosing different types of impairment in frontal brain function.
- Feature Extraction and Classification of Biosignals - Emotion Valence Detection from EEG SignalsPublication . Tomé, A. M.; Hidalgo-Muñoz, A.R.; López, M.M.; Teixeira, Ana; Santos, I.M.; Pereira, A.T.; Vázquez-Marrufo, M.; Lang, E.W.In this work a valence recognition system based on electroencephalograms is presented. The performance of the system is evaluated for two settings: single subjects (intra-subject) and between subjects (inter-subject). The feature extraction is based on measures of relative energies computed in short time intervals and certain frequency bands. The feature extraction is performed either on signals averaged over an ensemble of trials or on single-trial response signals. The subsequent classification stage is based on an ensemble classifier, i. e. a random forest of tree classifiers. The classification is performed considering the ensemble average responses of all subjects (inter-subject) or considering the single-trial responses of single subjects (intra-subject). Applying a proper importance measure of the classifier, feature elimination has been used to identify the most relevant features of the decision making.