Electrophysiological investigations of the timing of face processing

As important social stimuli, faces playa critical role in our lives. Much of our interaction with other people depends on our ability to recognize faces accurately. It has been proposed that face processing consists of different stages and interacts with other systems (Bruce & Young, 1986). At a perceptual level, the initial two stages, namely structural encoding and face recognition, are particularly relevant and are the focus of this dissertation. Event-related potentials (ERPs) are averaged EEG signals time-locked to a particular event (such as the presentation of a face). With their excellent temporal resolution, ERPs can provide important timing information about neural processes. Previous research has identified several ERP components that are especially related to face processing, including the N 170, the P2 and the N250. Their nature with respect to the stages of face processing is still unclear, and is examined in Studies 1 and 2. In Study 1, participants made gender decisions on a large set of female faces interspersed with a few male faces. The ERP responses to facial characteristics of the female faces indicated that the N 170 amplitude from each side of the head was affected by information from eye region and by facial layout: the right N 170 was affected by eye color and by face width, while the left N 170 was affected by eye size and by the relation between the sizes of the top and bottom parts of a face. In contrast, the P100 and the N250 components were largely unaffected by facial characteristics. These results thus provided direct evidence for the link between the N 170 and structural encoding of faces. In Study 2, focusing on the face recognition stage, we manipulated face identity strength by morphing individual faces to an "average" face. Participants performed a face identification task. The effect of face identity strength was found on the late P2 and the N250 components: as identity strength decreased from an individual face to the "average" face, the late P2 increased and the N250 decreased. In contrast, the P100, the N170 and the early P2 components were not affected by face identity strength. These results suggest that face recognition occurs after 200 ms, but not earlier. Finally, because faces are often associated with social information, we investigated in Study 3 how group membership might affect ERP responses to faces. After participants learned in- and out-group memberships of the face stimuli based on arbitrarily assigned nationality and university affiliation, we found that the N170 latency differentiated in-group and out-group faces, taking longer to process the latter. In comparison, without group memberships, there was no difference in N170 latency among the faces. This dissertation provides evidence that at a neural level, structural encoding of faces, indexed by the N170, occurs within 200 ms. Face recognition, indexed by the late P2 and the N250, occurs shortly afterwards between 200 and 300 ms. Social cognitive factors can also influence face processing. The effect is already evident as early as 130-200 ms at the structural encoding stage.

Towards natural human computer interaction in BCI

BCI systems require correct classification of signals interpreted from the brain for useful operation. To this end this paper investigates a method proposed in [1] to correctly classify a series of images presented to a group of subjects in [2]. We show that it is possible to use the proposed methods to correctly recognise the original stimuli presented to a subject from analysis of their EEG. Additionally we use a verification set to show that the trained classification method can be applied to a different set of data. We go on to investigate the issue of invariance in EEG signals. That is, the brain representation of similar stimuli is recognisable across different subjects. Finally we consider the usefulness of the methods investigated towards an improved BCI system and discuss how it could potentially lead to great improvements in the ease of use for the end user by offering an alternative, more intuitive control based mode of operation.

Toward construction of an inexpensive brain computer interface for goal oriented applications

The paper describes the implementation of an offline, low-cost Brain Computer Interface (BCI) alternative to more expensive commercial models. Using inexpensive general purpose clinical EEG acquisition hardware (Truscan32, Deymed Diagnostic) as the base unit, a synchronisation module was constructed to allow the EEG hardware to be operated precisely in time to allow for recording of automatically time stamped EEG signals. The synchronising module allows the EEG recordings to be aligned in stimulus time locked fashion for further processing by the classifier to establish the class of the stimulus, sample by sample. This allows for the acquisition of signals from the subject’s brain for the goal oriented BCI application based on the oddball paradigm. An appropriate graphical user interface (GUI) was constructed and implemented as the method to elicit the required responses (in this case Event Related Potentials or ERPs) from the subject.

Modulation of event-related desynchronization during kinematic and kinetic hand movements

Background Event-related desynchronization/synchronization (ERD/ERS) is a relative power decrease/increase of electroencephalogram (EEG) in a specific frequency band during physical motor execution and mental motor imagery, thus it is widely used for the brain-computer interface (BCI) purpose. However what the ERD really reflects and its frequency band specific role have not been agreed and are under investigation. Understanding the underlying mechanism which causes a significant ERD would be crucial to improve the reliability of the ERD-based BCI. We systematically investigated the relationship between conditions of actual repetitive hand movements and resulting ERD. Methods Eleven healthy young participants were asked to close/open their right hand repetitively at three different speeds (Hold, 1/3 Hz, and 1 Hz) and four distinct motor loads (0, 2, 10, and 15 kgf). In each condition, participants repeated 20 experimental trials, each of which consisted of rest (8–10 s), preparation (1 s) and task (6 s) periods. Under the Hold condition, participants were instructed to keep clenching their hand (i.e., isometric contraction) during the task period. Throughout the experiment, EEG signals were recorded from left and right motor areas for offline data analysis. We obtained time courses of EEG power spectrum to discuss the modulation of mu and beta-ERD/ERS due to the task conditions. Results We confirmed salient mu-ERD (8–13 Hz) and slightly weak beta-ERD (14–30 Hz) on both hemispheres during repetitive hand grasping movements. According to a 3 × 4 ANOVA (speed × motor load), both mu and beta-ERD during the task period were significantly weakened under the Hold condition, whereas no significant difference in the kinetics levels and interaction effect was observed. Conclusions This study investigates the effect of changes in kinematics and kinetics on resulting ERD during repetitive hand grasping movements. The experimental results suggest that the strength of ERD may reflect the time differentiation of hand postures in motor planning process or the variation of proprioception resulting from hand movements, rather than the motor command generated in the down stream, which recruits a group of motor neurons.

Motor imagery data classification for BCI application using wavelet packet feature extraction

The noninvasive brain imaging modalities have provided us an extraordinary means for monitoring the working brain. Among these modalities, Electroencephalography (EEG) is the most widely used technique for measuring the brain signals under different tasks, due to its mobility, low cost, and high temporal resolution. In this paper we investigate the use of EEG signals in brain-computer interface (BCI) systems.

Fuzzy system with tabu search learning for classification of motor imagery data

This paper introduces an approach to classify EEG signals using wavelet transform and a fuzzy standard additive model (FSAM) with tabu search learning mechanism. Wavelet coefficients are ranked based on statistics of the Wilcoxon test. The most informative coefficients are assembled to form a feature set that serves as inputs to the tabu-FSAM. Two benchmark datasets, named Ia and Ib, downloaded from the brain-computer interface (BCI) competition II are employed for the experiments. Classification performance is evaluated using accuracy, mutual information, Gini coefficient and F-measure. Widely-used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, ensemble learning Adaboost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The proposed tabu-FSAM method considerably dominates the competitive classifiers, and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II.

Biomedical signal filtering for noisy environments

 Luke's work addresses issue of robustly attenuating multi-source noise from surface EEG signals using a novel Adaptive-Multiple-Reference Least-Means-Squares filter (AMR-LMS). In practice, the filter successfully removes electrical interference and muscle noise generated during movement which contaminates EEG, allowing subjects to maintain maximum mobility throughout signal acquisition and during the use of a Brain Computer Interface.

Prosthetic motor imaginary task classification using single channel of electroencephalography

Brain Computer Interface (BCI) is playing a very important role in human machine communications. Recent communication systems depend on the brain signals for communication. In these systems, users clearly manipulate their brain activity rather than using motor movements in order to generate signals that could be used to give commands and control any communication devices, robots or computers. In this paper, the aim was to estimate the performance of a brain computer interface (BCI) system by detecting the prosthetic motor imaginary tasks by using only a single channel of electroencephalography (EEG). The participant is asked to imagine moving his arm up or down and our system detects the movement based on the participant brain signal. Some features are extracted from the brain signal using Mel-Frequency Cepstrum Coefficient and based on these feature a Hidden Markov model is used to help in knowing if the participant imagined moving up or down. The major advantage in our method is that only one channel is needed to take the decision. Moreover, the method is online which means that it can give the decision as soon as the signal is given to the system. Hundred signals were used for testing, on average 89 % of the up down prosthetic motor imaginary tasks were detected correctly. This method can be used in many different applications such as: moving artificial prosthetic limbs and wheelchairs due to it's high speed and accuracy.

Electrophysiological effects of guanosine and MK-801 in a quinolinic acid-induced seizure model

TORRES, F ; FILHO, M.S. ; ANTUNES, C. ; KALININE, E. ; ANTONIOLLI, E. ; PORTELA, Luis Valmor ; SOUZA, Diogo Onofre ; TORT, A. B. L. . Electrophysiological effects of guanosine and MK-801 in a quinolinic acid-induced seizure model. Experimental Neurology , v. 221, p. 296-306, 2010

Aspectos epidemiológicos, cognitivo-comportamentais e neurofisiológicos do sonho lúcido

Lucid dreaming (LD) is a mental state in which the subject is aware of being dreaming while dreaming. The prevalence of LD among Europeans, North Americans and Asians is quite variable (between 26 and 92%) (Stepansky et al., 1998; Schredl & Erlacher, 2011; Yu, 2008); in Latin Americans it is yet to be investigated. Furthermore, the neural bases of LD remain controversial. Different studies have observed that LD presents power increases in the alpha frequency band (Tyson et al., 1984), in beta oscillations recorded from the parietal cortex (Holzinger et al., 2006) and in gamma rhythm recorded from the frontal cortex (Voss et al., 2009), in comparison with non-lucid dreaming. In this thesis we report epidemiological and neurophysiological investigations of LD. To investigate the epidemiology of LD (Study 1), we developed an online questionnaire about dreams that was answered by 3,427 volunteers. In this sample, 56% were women, 24% were men and 20% did not inform their gender (the median age was 25 years). A total of 76.5% of the subjects reported recalling dreams at least once a week, and about two-thirds of them reported dreaming always in the first person, i.e. when the dreamer observes the dream from within itself, not as another dream character. Dream reports typically depicted actions (93.3%), known people (92.9%), sounds/voices (78.5%), and colored images (76.3%). The oneiric content was related to plans for upcoming days (37.8%), and memories of the previous day (13.8%). Nightmares were characterized by general anxiety/fear (65.5%), feeling of being chased (48.5%), and non-painful unpleasant sensations (47.6%). With regard to LD, 77.2% of the subjects reported having experienced LD at least once in their lifetime (44.9% reported up to 10 episodes ever). LD frequency was weakly correlated with dream recall frequency (r = 0.20, p <0.001) and was higher in men (χ2=10.2, p=0.001). The control of LD was rare (29.7%) and inversely correlated with LD duration (r=-0.38, p <0.001), which is usually short: to 48.5% of the subjects, LD takes less than 1 minute. LD occurrence is mainly associated with having sleep without a fixed time to wake up (38.3%), which increases the chance of having REM sleep (REMS). LD is also associated with stress (30.1%), which increases REMS transitions into wakefulness. Overall, the data suggest that dreams and nightmares can be evolutionarily understood as a simulation of the common situations that happen in life, and that are related to our social, psychological and biological integrity. The results also indicate that LD is a relatively common experience (but not recurrent), often elusive and difficult to control, suggesting that LD is an incomplete stationary stage (or phase transition) between REMS and wake state. Moreover, despite the variability of LD prevalence among North Americans, Europeans and Asians, our data from Latin Americans strengthens the notion that LD is a general phenomenon of the human species. To further investigate the neural bases of LD (Study 2), we performed sleep recordings of 32 non-frequent lucid dreamers (sample 1) and 6 frequent lucid dreamers (sample 2). In sample 1, we applied two cognitive-behavioral techniques to induce LD: presleep LD suggestion (n=8) and light pulses applied during REMS (n=8); in a control group we made no attempt to influence dreaming (n=16). The results indicate that it is quite difficult but still possible to induce LD, since we could induce LD in a single subject, using the suggestion technique. EEG signals from this one subject exhibited alpha (7-14 Hz) bursts prior to LD. These bursts were brief (about 3s), without significant change in muscle tone, and independent of the presence of rapid eye movements. No such bursts were observed in the remaining 31 subjects. In addition, LD exhibited significantly higher occipital alpha and right temporo-parietal gamma (30-50 Hz) power, in comparison with non-lucid REMS. In sample 2, LD presented increased frontal high-gamma (50-100 Hz) power on average, in comparison with non-lucid REMS; however, this was not consistent across all subjects, being a clear phenomenon in just one subject. We also observed that four of these volunteers showed an increase in alpha rhythm power over the occipital region, immediately before or during LD. Altogether, our preliminary results suggest that LD presents neurophysiological characteristics that make it different from both waking and the typical REMS. To the extent that the right temporo-parietal and frontal regions are related to the formation of selfconsciousness and body internal image, we suggest that an increased activity in these regions during sleep may be the neurobiological mechanism underlying LD. The alpha rhythm bursts, as well as the alpha power increase over the occipital region, may represent micro-arousals, which facilitate the contact of the brain during sleep with the external environment, favoring the occurrence of LD. This also strengthens the notion that LD is an intermediary state between sleep and wakefulness

Uso de Redes Neurais ARTMAP Nebulosas para a classificação de padrões em sinais ECoG relacionados ao movimento dos dedos

Pós-graduação em Engenharia Elétrica - FEIS

Brain-Computer Interfaces for augmented communication: Asynchronous and adaptive algorithms and evaluation with end users

This thesis aimed at addressing some of the issues that, at the state of the art, avoid the P300-based brain computer interface (BCI) systems to move from research laboratories to end users’ home. An innovative asynchronous classifier has been defined and validated. It relies on the introduction of a set of thresholds in the classifier, and such thresholds have been assessed considering the distributions of score values relating to target, non-target stimuli and epochs of voluntary no-control. With the asynchronous classifier, a P300-based BCI system can adapt its speed to the current state of the user and can automatically suspend the control when the user diverts his attention from the stimulation interface. Since EEG signals are non-stationary and show inherent variability, in order to make long-term use of BCI possible, it is important to track changes in ongoing EEG activity and to adapt BCI model parameters accordingly. To this aim, the asynchronous classifier has been subsequently improved by introducing a self-calibration algorithm for the continuous and unsupervised recalibration of the subjective control parameters. Finally an index for the online monitoring of the EEG quality has been defined and validated in order to detect potential problems and system failures. This thesis ends with the description of a translational work involving end users (people with amyotrophic lateral sclerosis-ALS). Focusing on the concepts of the user centered design approach, the phases relating to the design, the development and the validation of an innovative assistive device have been described. The proposed assistive technology (AT) has been specifically designed to meet the needs of people with ALS during the different phases of the disease (i.e. the degree of motor abilities impairment). Indeed, the AT can be accessed with several input devices either conventional (mouse, touchscreen) or alterative (switches, headtracker) up to a P300-based BCI.

Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients

To derive tests for randomness, nonlinear-independence, and stationarity, we combine surrogates with a nonlinear prediction error, a nonlinear interdependence measure, and linear variability measures, respectively. We apply these tests to intracranial electroencephalographic recordings (EEG) from patients suffering from pharmacoresistant focal-onset epilepsy. These recordings had been performed prior to and independent from our study as part of the epilepsy diagnostics. The clinical purpose of these recordings was to delineate the brain areas to be surgically removed in each individual patient in order to achieve seizure control. This allowed us to define two distinct sets of signals: One set of signals recorded from brain areas where the first ictal EEG signal changes were detected as judged by expert visual inspection ("focal signals") and one set of signals recorded from brain areas that were not involved at seizure onset ("nonfocal signals"). We find more rejections for both the randomness and the nonlinear-independence test for focal versus nonfocal signals. In contrast more rejections of the stationarity test are found for nonfocal signals. Furthermore, while for nonfocal signals the rejection of the stationarity test increases the rejection probability of the randomness and nonlinear-independence test substantially, we find a much weaker influence for the focal signals. In consequence, the contrast between the focal and nonfocal signals obtained from the randomness and nonlinear-independence test is further enhanced when we exclude signals for which the stationarity test is rejected. To study the dependence between the randomness and nonlinear-independence test we include only focal signals for which the stationarity test is not rejected. We show that the rejection of these two tests correlates across signals. The rejection of either test is, however, neither necessary nor sufficient for the rejection of the other test. Thus, our results suggest that EEG signals from epileptogenic brain areas are less random, more nonlinear-dependent, and more stationary compared to signals recorded from nonepileptogenic brain areas. We provide the data, source code, and detailed results in the public domain.

Wavelet-denoising of electroencephalogram and the absolute slope method: a new tool to improve electroencephalographic localization and lateralization

OBJECTIVE: In ictal scalp electroencephalogram (EEG) the presence of artefacts and the wide ranging patterns of discharges are hurdles to good diagnostic accuracy. Quantitative EEG aids the lateralization and/or localization process of epileptiform activity. METHODS: Twelve patients achieving Engel Class I/IIa outcome following temporal lobe surgery (1 year) were selected with approximately 1-3 ictal EEGs analyzed/patient. The EEG signals were denoised with discrete wavelet transform (DWT), followed by computing the normalized absolute slopes and spatial interpolation of scalp topography associated to detection of local maxima. For localization, the region with the highest normalized absolute slopes at the time when epileptiform activities were registered (>2.5 times standard deviation) was designated as the region of onset. For lateralization, the cerebral hemisphere registering the first appearance of normalized absolute slopes >2.5 times the standard deviation was designated as the side of onset. As comparison, all the EEG episodes were reviewed by two neurologists blinded to clinical information to determine the localization and lateralization of seizure onset by visual analysis. RESULTS: 16/25 seizures (64%) were correctly localized by the visual method and 21/25 seizures (84%) by the quantitative EEG method. 12/25 seizures (48%) were correctly lateralized by the visual method and 23/25 seizures (92%) by the quantitative EEG method. The McNemar test showed p=0.15 for localization and p=0.0026 for lateralization when comparing the two methods. CONCLUSIONS: The quantitative EEG method yielded significantly more seizure episodes that were correctly lateralized and there was a trend towards more correctly localized seizures. SIGNIFICANCE: Coupling DWT with the absolute slope method helps clinicians achieve a better EEG diagnostic accuracy.

Detecting Functional Hubs of Ictogenic Networks

Quantitative EEG (qEEG) has modified our understanding of epileptic seizures, shifting our view from the traditionally accepted hyper-synchrony paradigm toward more complex models based on re-organization of functional networks. However, qEEG measurements are so far rarely considered during the clinical decision-making process. To better understand the dynamics of intracranial EEG signals, we examine a functional network derived from the quantification of information flow between intracranial EEG signals. Using transfer entropy, we analyzed 198 seizures from 27 patients undergoing pre-surgical evaluation for pharmaco-resistant epilepsy. During each seizure we considered for each network the in-, out- and total "hubs", defined respectively as the time and the EEG channels with the maximal incoming, outgoing or total (bidirectional) information flow. In the majority of cases we found that the hubs occur around the middle of seizures, and interestingly not at the beginning or end, where the most dramatic EEG signal changes are found by visual inspection. For the patients who then underwent surgery, good postoperative clinical outcome was on average associated with a higher percentage of out- or total-hubs located in the resected area (for out-hubs p = 0.01, for total-hubs p = 0.04). The location of in-hubs showed no clear predictive value. We conclude that the study of functional networks based on qEEG measurements may help to identify brain areas that are critical for seizure generation and are thus potential targets for focused therapeutic interventions.