Browsing by Author "Santos, I.M."
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- Clustering evoked potential signals using subspace methodsPublication . Tomé, A. M.; Teixeira, Ana; Figueiredo, N.; Georgieva, P.; Santos, I.M.; Lang, E.This work proposes a clustering technique to analyze evoked potential signals. The proposed method uses an orthogonal subspace model to enhance the single-trial signals of a session and simultaneously a subspace measure to group the trials into clusters. The ensemble averages of the signals of the different clusters are compared with ensemble averages of visually selected trials which are free of any artifact. Preliminary results consider recordings from an occipital channel where evoked response P100 wave is most pronounced.
- 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.
- Mining EEG scalp maps of independent components related to HCT tasksPublication . Teixeira, Ana; Santos, I.M.; Lang, E.W.; Tome, A.M.This work presents an unsupervised mining strat- egy, applied to an independent component analysis (ICA) of segments of data collected while participants are answering to the items of the Halstead Category Test (HCT). This new methodology was developed to achieve signal components at trial level and therefore to study signal dynamics which are not available within participants’ ensemble average signals. The study will be focused on the signal component that can be elicited by the binary visual feedback which is part of the HCT protocol. The experimental study is conducted using a cohort of 58 participants.
- SSA of biomedical signals: A linear invariant systems approachPublication . Figueiredo, N.; Georgieva, P.; Lang, E.W.; Santos, I.M.; Teixeira, A.R.; Tomé, A.M.Singular spectrum analysis (SSA) is considered from a linear invariant systems perspective. In this terminology, the extracted components are considered as outputs of a linear invariant system which corresponds to finite impulse response (FIR) filters. The number of filters is determined by the embedding dimension.We propose to explicitly define the frequency response of each filter responsible for the selection of informative components. We also introduce a subspace distance measure for clustering subspace models. We illustrate the methodology by analyzing Electroencephalograms (EEG).