EEG correlates of social interaction at distance

This study investigated EEG correlates of social interaction at distance between twenty-five pairs of participants who were not connected by any traditional channels of communication. Each session involved the application of 128 stimulations separated by intervals of random duration ranging from 4 to 6 seconds. One of the pair received a one-second stimulation from a light signal produced by an arrangement of red LEDs, and a simultaneous 500 Hz sinusoidal audio signal of the same length. The other member of the pair sat in an isolated sound-proof room, such that any sensory interaction between the pair was impossible. An analysis of the Event-Related Potentials associated with sensory stimulation using traditional averaging methods showed a distinct peak at approximately 300 ms, but only in the EEG activity of subjects who were directly stimulated. However, when a new algorithm was applied to the EEG activity based on the correlation between signals from all active electrodes, a weak but robust response was also detected in the EEG activity of the passive member of the pair, particularly within 9 – 10 Hz in the Alpha range. Using the Bootstrap method and the Monte Carlo emulation, this signal was found to be statistically significant

Malleability of the self: electrophysiological correlates of the enfacement illusion

Self-face representation is fundamentally important for self-identity and self-consciousness. Given its role in preserving identity over time, self-face processing is considered as a robust and stable process. Yet, recent studies indicate that simple psychophysics manipulations may change how we process our own face. Specifically, experiencing tactile facial stimulation while seeing similar synchronous stimuli delivered to the face of another individual seen as in a mirror, induces 'enfacement' illusion, i.e. the subjective experience of ownership of the other’s face and a bias in attributing to the self, facial features of the other person. Here we recorded visual Event-Related Potentials elicited by the presentation of self, other and morphed faces during a self-other discrimination task performed immediately after participants received synchronous and control asynchronous Interpersonal Multisensory Stimulation (IMS). We found that self-face presentation after synchronous as compared to asynchronous stimulation significantly reduced the late positive potential (LPP; 450-750 ms), a reliable electrophysiological marker of self-identification processes. Additionally, enfacement cancelled out the differences in LPP amplitudes produced by self- and other-face during the control condition. These findings represent the first direct neurophysiological evidence that enfacement may affect self-face processing and pave the way to novel paradigms for exploring defective self-representation and self-other interactions

Biomedical signal identification and analysis

In the article there have been presented methods of measuring and analysis biological signals, which may be used as signals control mechanical system. Among others, ther have been decribed the usage of EEG (electroencephalographic signal). Like in the case of other signals, the analysis of bio-medical signals most often resolves itself to the frequency analysis of their content with the help of Fourier transformation, and their processing the most often has a form of frequency filtering; in other words, removing from a signal its components with defined frequencies, for example, interferences. The researches have two parts. In the first part date was generated in Lab View program, and next the analysis was done (it was an example of EEG signal). In the next part the EEG signal was measured using 32 channels apertures and next real signal was analyzed using Lab View

EEG Biometrics: On the Use of Occipital Cortex Based Features from Visual Evoked Potentials

The potential of using Electro-Encephalo-Gram (EEG) data as a biometric identifier is studied. This is the first study that assesses looming stimuli for the creation of biometrically useful Visual Evoked Potentials (VEP), i.e. EEG responses due to visual stimuli. A novel method for the detection of VEP responses with minimal expert interaction is introduced. The EEG data, segmented based on the VEP, are used to create a reliable feature vector. In contrast to previous studies, we provide a publicly available evaluation dataset based on infants which is therefore not biased due to unhealthy individuals. Only data from the occipital cortex are used (i.e. about 3 of the many possible electrode positions in the scalp), making the potential EEG biometric capture devices relatively simpler

EEG correlates of social interaction at distance

This study investigated EEG correlates of social interaction at distance between twenty-five pairs of participants who were not connected by any traditional channels of communication. Each session involved the application of 128 stimulations separated by intervals of random duration ranging from 4 to 6 seconds. One of the pair received a one-second stimulation from a light signal produced by an arrangement of red LEDs, and a simultaneous 500 Hz sinusoidal audio signal of the same length. The other member of the pair sat in an isolated sound-proof room, such that any sensory interaction between the pair was impossible. An analysis of the Event-Related Potentials associated with sensory stimulation using traditional averaging methods showed a distinct peak at approximately 300 ms, but only in the EEG activity of subjects who were directly stimulated. However, when a new algorithm was applied to the EEG activity based on the correlation between signals from all active electrodes, a weak but robust response was also detected in the EEG activity of the passive member of the pair, particularly within 9 – 10 Hz in the Alpha range. Using the Bootstrap method and the Monte Carlo emulation, this signal was found to be statistically significant

Affective Brain-Computer Interfaces Neuroscientific Approaches to Affect Detection

The brain is involved in the registration, evaluation, and representation of emotional events, and in the subsequent planning and execution of adequate actions. Novel interface technologies – so-called affective brain-computer interfaces (aBCI) - can use this rich neural information, occurring in response to affective stimulation, for the detection of the affective state of the user. This chapter gives an overview of the promises and challenges that arise from the possibility of neurophysiology-based affect detection, with a special focus on electrophysiological signals. After outlining the potential of aBCI relative to other sensing modalities, the reader is introduced to the neurophysiological and neurotechnological background of this interface technology. Potential application scenarios are situated in a general framework of brain-computer interfaces. Finally, the main scientific and technological challenges that have to be solved on the way toward reliable affective brain-computer interfaces are discussed

On the Usability of Electroencephalographic Signals for Biometric Recognition: A Survey

Research on using electroencephalographic signals for biometric recognition has made considerable progress and is attracting growing attention in recent years. However, the usability aspects of the proposed biometric systems in the literatures have not received significant attention. In this paper, we present a comprehensive survey to examine the development and current status of various aspects of electroencephalography (EEG)-based biometric recognition. We first compare the characteristics of different stimuli that have been used for evoking biometric information bearing EEG signals. This is followed by a survey of the reported features and classifiers employed for EEG biometric recognition. To highlight the usability challenges of using EEG for biometric recognition in real-life scenarios, we propose a novel usability assessment framework which combines a number of user-related factors to evaluate the reported systems. The evaluation scores indicate a pattern of increasing usability, particularly in recent years, of EEG-based biometric systems as efforts have been made to improve the performance of such systems in realistic application scenarios. We also propose how this framework may be extended to take into account Aging effects as more performance data becomes available