Age-induced differences in brain neural activation elicited by visual emotional stimuli: A high-density EEG study.

Identifying the brain sources of neural activation during processing of emotional information remains a very challenging task. In this work, we investigated the response to different emotional stimuli and the effect of age on the neuronal activation. Two negative emotion conditions, i.e., 'anger' and 'fear' faces were presented to 22 adult female participants (11 young and 11 elderly) while acquiring high-density electroencephalogram (EEG) data of 256 channels. Brain source localization was utilized to study the modulations in the early N170 event-related-potential component. The results revealed alterations in the amplitude of N170 and the localization of areas with maximum neural activation. Furthermore, age-induced differences are shown in the topographic maps and the neural activation for both emotional stimuli. Overall, aging appeared to affect the limbic area and its implication to emotional processing. These findings can serve as a step toward the understanding of the way the brain functions and evolves with age which is a significant element in the design of assistive environments.

Cognitive MMN and P300 in mild cognitive impairment and Alzheimer's disease: A high density EEG-3D vector field tomography approach.

Precise preclinical detection of dementia for effective treatment and stage monitoring is of great importance. Miscellaneous types of biomarkers, e.g., biochemical, genetic, neuroimaging, and physiological, have been proposed to diagnose Alzheimer's disease (AD), the usual suspect behind manifested cognitive decline, and mild cognitive impairment (MCI), a neuropathology prior to AD that does not affect cognitive functions. Event related potential (ERP) methods constitute a non-invasive, inexpensive means of analysis and have been proposed as sensitive biomarkers of cognitive impairment; besides, various ERP components are strongly linked with working memory, attention, sensory processing and motor responses. In this study, an auditory oddball task is employed, to acquire high density electroencephalograhy recordings from healthy elderly controls, MCI and AD patients. The mismatch negativity (MMN) and P300 ERP components are then extracted and their relationship with neurodegeneration is examined. Then, the neural activation at these components is reconstructed using the 3D vector field tomography (3D-VFT) inverse solution. The results reveal a decline of both ERPs amplitude, and a statistically significant prolongation of their latency as cognitive impairment advances. For the MMN, higher brain activation is usually localized in the inferior frontal and superior temporal gyri in the controls. However, in AD, parietal sites exhibit strong activity. Stronger P300 generators are mostly found in the frontal lobe for the controls, but in AD they often shift to the temporal lobe. Reduction in inferior frontal source strength and the switch of the maximum intensity area to parietal and superior temporal sites suggest that these areas, especially the former, are of particular significance when neurodegenerative disorders are investigated. The modulation of MMN and P300 can serve to produce biomarkers of dementia and its progression, and brain imaging can further contribute to the diagnostic efficiency of ERPs.

Gender effect in human brain responses to bottom-up and top-down attention using the EEG 3D-Vector Field Tomography.

The effect of gender in rapidly allocating attention to objects, features or locations, as reflected in brain activity, is examined in this study. A visual-attention task, consisting of bottom-up (visual pop-out) and top-down (visual search) conditions during stimuli of four triangles, i.e., a target and three distractors, was engaged. In pop-out condition, both color and orientation of the distractors differed from target, while in search condition they differed only in orientation. During the task, high-density EEG (256 channels) data were recorded and analyzed by means of behavioral, event-related potentials, i.e., the P300 component and brain source localization analysis using 3D-Vector Field Tomography (3D-VFT). Twenty subjects (half female; 32±4.7 years old) participated in the experiments, performing 60 trials for each condition. Behavioral analysis revealed that both female and male outperformed in the pop-out condition compared to the search one, with respect to accuracy and reaction time, whereas no gender-related statistical significant differences were found. Nevertheless, in the search condition, higher P300 amplitudes were detected for females compared to males (p <; 7 · 10(-3)). Moreover, the findings suggested that the maximum activation in females was located mainly in the left inferior frontal and superior temporal gyri, whereas in males it was found in the right inferior frontal and superior temporal gyri. Overall, the experimental results show that visual attention depends on contributions from different brain lateralization linked to gender, posing important implications in studying developmental disorders, characterized by gender differences.

Phase-amplitude cross-frequency coupling in EEG-derived cortical time series upon an auditory perception task.

Recent evidence suggests that cross-frequency coupling (CFC) plays an essential role in multi-scale communication across the brain. The amplitude of the high frequency oscillations, responsible for local activity, is modulated by the phase of the lower frequency activity, in a task and region-relevant way. In this paper, we examine this phase-amplitude coupling in a two-tone oddball paradigm for the low frequency bands (delta, theta, alpha, and beta) and determine the most prominent CFCs. Data consisted of cortical time series, extracted by applying three-dimensional vector field tomography (3D-VFT) to high density (256 channels) electroencephalography (HD-EEG), and CFC analysis was based on the phase-amplitude coupling metric, namely PAC. Our findings suggest CFC spanning across all brain regions and low frequencies. Stronger coupling was observed in the delta band, that is closely linked to sensory processing. However, theta coupling was reinforced in the target tone response, revealing a task-dependent CFC and its role in brain networks communication.

Using sample entropy for automated sign language recognition on sEMG and accelerometer data.

Communication using sign language (SL) provides alternative means for information transmission among the deaf. Automated gesture recognition involved in SL, however, could further expand this communication channel to the world of hearers. In this study, data from five-channel surface electromyogram and three-dimensional accelerometer from signers' dominant hand were subjected to a feature extraction process. The latter consisted of sample entropy (SampEn)-based analysis, whereas time-frequency feature (TFF) analysis was also performed as a baseline method for the automated recognition of 60-word lexicon Greek SL (GSL) isolated signs. Experimental results have shown a 66 and 92% mean classification accuracy threshold using TFF and SampEn, respectively. These results justify the superiority of SampEn against conventional methods, such as TFF, to provide with high recognition hit-ratios, combined with feature vector dimension reduction, toward a fast and reliable automated GSL gesture recognition.

Sign language recognition using intrinsic-mode sample entropy on sEMG and accelerometer data.

Sign language forms a communication channel among the deaf; however, automated gesture recognition could further expand their communication with the hearers. In this work, data from five-channel surface electromyogram and 3-D accelerometer from the signer's dominant hand were analyzed using intrinsic-mode entropy (IMEn) for the automated recognition of Greek sign language (GSL) isolated signs. Discriminant analysis was used to identify the effective scales of the intrinsic-mode functions and the window length for the calculation of the IMEn that contributes to the efficient classification of the GSL signs. Experimental results from the IMEn analysis applied to GSL signs corresponding to 60-word lexicon repeated ten times by three native signers have shown more than 93% mean classification accuracy using IMEn as the only source of the classification feature set. This provides a promising bed-set toward the automated GSL gesture recognition.

Intrinsic mode entropy: an enhanced classification means for automated Greek Sign Language gesture recognition.

Sign language forms a communication channel among the deaf; however, automated gesture recognition could further expand their communication with the hearers. In this work, data from three-dimensional accelerometer and five-channel surface electromyogram of the user's dominant forearm are analyzed using intrinsic mode entropy (IMEn) for the automated recognition of Greek Sign Language (GSL) gestures. IMEn was estimated for various window lengths and evaluated by the Mahalanobis distance criterion. Discriminant analysis was used to identify the effective scales of the intrinsic mode functions and the window length for the calculation of the IMEn that contributes to the correct classification of the GSL gestures. Experimental results from the IMEn analysis of GSL gestures corresponding to ten words have shown 100% classification accuracy using IMEn as the only classification feature. This provides a promising bed-set towards the automated GSL gesture recognition.

Evaluation of surface EMG features for the recognition of American Sign Language gestures.

In this work, analysis of the surface electromyogram (sEMG) signal is proposed for the recognition of American sign language (ASL) gestures. To this purpose, sixteen features are extracted from the sEMG signal acquired from the user's forearm, and evaluated by the Mahalanobis distance criterion. Discriminant analysis is used to reduce the number of features used in the classification of the signed ASL gestures. The proposed features are tested against noise resulting in a further reduced set of features, which are evaluated for their discriminant ability. The classification results reveal that 97.7% of the inspected ASL gestures were correctly recognized using sEMG-based features, providing a promising solution to the automatic ASL gesture recognition problem.