Commenced in January 2007
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Edition: International
Paper Count: 6

brain activity Related Publications

6 Spatiotemporal Analysis of Visual Evoked Responses Using Dense EEG

Authors: Rima Hleiss, Elie Bitar, Mahmoud Hassan, Mohamad Khalil

Abstract:

A comprehensive study of object recognition in the human brain requires combining both spatial and temporal analysis of brain activity. Here, we are mainly interested in three issues: the time perception of visual objects, the ability of discrimination between two particular categories (objects vs. animals), and the possibility to identify a particular spatial representation of visual objects. Our experiment consisted of acquiring dense electroencephalographic (EEG) signals during a picture-naming task comprising a set of objects and animals’ images. These EEG responses were recorded from nine participants. In order to determine the time perception of the presented visual stimulus, we analyzed the Event Related Potentials (ERPs) derived from the recorded EEG signals. The analysis of these signals showed that the brain perceives animals and objects with different time instants. Concerning the discrimination of the two categories, the support vector machine (SVM) was applied on the instantaneous EEG (excellent temporal resolution: on the order of millisecond) to categorize the visual stimuli into two different classes. The spatial differences between the evoked responses of the two categories were also investigated. The results showed a variation of the neural activity with the properties of the visual input. Results showed also the existence of a spatial pattern of electrodes over particular regions of the scalp in correspondence to their responses to the visual inputs.

Keywords: Perception, SVM, brain activity, dense EEG, evoked responses, spatiotemporal analysis

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5 Behavioral and EEG Reactions in Native Turkic-Speaking Inhabitants of Siberia and Siberian Russians during Recognition of Syntactic Errors in Sentences in Native and Foreign Languages

Authors: Tatiana N. Astakhova, Alexander E. Saprygin, Tatiana A. Golovko, Alexander N. Savostyanov, Mikhail S. Vlasov, Natalia V. Borisova, Alexandera G. Karpova, Urana N. Kavai-ool, Elena Mokur-ool, Nikolay A. Kolchano, Lyubomir I. Aftanas

Abstract:

The aim of the study is to compare behavioral and EEG reactions in Turkic-speaking inhabitants of Siberia (Tuvinians and Yakuts) and Russians during the recognition of syntax errors in native and foreign languages. Sixty-three healthy aboriginals of the Tyva Republic, 29 inhabitants of the Sakha (Yakutia) Republic, and 55 Russians from Novosibirsk participated in the study. EEG were recorded during execution of error-recognition task in Russian and English language (in all participants) and in native languages (Tuvinian or Yakut Turkic-speaking inhabitants). Reaction time (RT) and quality of task execution were chosen as behavioral measures. Amplitude and cortical distribution of P300 and P600 peaks of ERP were used as a measure of speech-related brain activity. In Tuvinians, there were no differences in the P300 and P600 amplitudes as well as in cortical topology for Russian and Tuvinian languages, but there was a difference for English. In Yakuts, the P300 and P600 amplitudes and topology of ERP for Russian language were the same as Russians had for native language. In Yakuts, brain reactions during Yakut and English language comprehension had no difference, while the Russian language comprehension was differed from both Yakut and English. We found out that the Tuvinians recognized both Russian and Tuvinian as native languages, and English as a foreign language. The Yakuts recognized both English and Yakut as foreign languages, but Russian as a native language. According to the inquirer, both Tuvinians and Yakuts use the national language as a spoken language, whereas they do not use it for writing. It can well be a reason that Yakuts perceive the Yakut writing language as a foreign language while writing Russian as their native.

Keywords: eeg, brain activity, syntactic analysis, native and foreign language

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4 EEG Correlates of Trait and Mathematical Anxiety during Lexical and Numerical Error-Recognition Tasks

Authors: Alexander N. Savostyanov, Tatiana A. Dolgorukova, Elena A. Esipenko, Mikhail S. Zaleshin, Margherita Malanchini, Anna V. Budakova, Alexander E. Saprygin, Tatiana A. Golovko, Yulia V. Kovas

Abstract:

EEG correlates of mathematical and trait anxiety level were studied in 52 healthy Russian-speakers during execution of error-recognition tasks with lexical, arithmetic and algebraic conditions. Event-related spectral perturbations were used as a measure of brain activity. The ERSP plots revealed alpha/beta desynchronizations within a 500-3000 ms interval after task onset and slow-wave synchronization within an interval of 150-350 ms. Amplitudes of these intervals reflected the accuracy of error recognition, and were differently associated with the three conditions. The correlates of anxiety were found in theta (4-8 Hz) and beta2 (16- 20 Hz) frequency bands. In theta band the effects of mathematical anxiety were stronger expressed in lexical, than in arithmetic and algebraic condition. The mathematical anxiety effects in theta band were associated with differences between anterior and posterior cortical areas, whereas the effects of trait anxiety were associated with inter-hemispherical differences. In beta1 and beta2 bands effects of trait and mathematical anxiety were directed oppositely. The trait anxiety was associated with increase of amplitude of desynchronization, whereas the mathematical anxiety was associated with decrease of this amplitude. The effect of mathematical anxiety in beta2 band was insignificant for lexical condition but was the strongest in algebraic condition. EEG correlates of anxiety in theta band could be interpreted as indexes of task emotionality, whereas the reaction in beta2 band is related to tension of intellectual resources.

Keywords: eeg, brain activity, mathematical and trait anxiety, lexical and numerical error-recognition tasks

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3 EEG Indices to Time-On-Task Effects and to a Workload Manipulation (Cueing)

Authors: A. T. Kamzanova, G. Matthews, A. M. Kustubayeva, S. M. Jakupov

Abstract:

The aim of this study was to evaluate the sensitivity of a range of EEG indices to time-on-task effects and to a workload manipulation (cueing), during performance of a resource-limited vigilance task. Effects of task period and cueing on performance and subjective state response were consistent with previous vigilance studies and with resource theory. Two EEG indices – the Task Load Index (TLI) and global lower frequency (LF) alpha power – showed effects of task period and cueing similar to those seen with correct detections. Across four successive task periods, the TLI declined and LF alpha power increased. Cueing increased TLI and decreased LF alpha. Other indices – the Engagement Index (EI), frontal theta and upper frequency (UF) alpha failed to show these effects. However, EI and frontal theta were sensitive to interactive effects of task period and cueing, which may correspond to a stronger anxiety response to the uncued task.

Keywords: eeg, brain activity, task engagement, vigilance task

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2 Functional Near Infrared Spectroscope for Cognition Brain Tasks by Wavelets Analysis and Neural Networks

Authors: Truong Quang Dang Khoa, Masahiro Nakagawa

Abstract:

Brain Computer Interface (BCI) has been recently increased in research. Functional Near Infrared Spectroscope (fNIRs) is one the latest technologies which utilize light in the near-infrared range to determine brain activities. Because near infrared technology allows design of safe, portable, wearable, non-invasive and wireless qualities monitoring systems, fNIRs monitoring of brain hemodynamics can be value in helping to understand brain tasks. In this paper, we present results of fNIRs signal analysis indicating that there exist distinct patterns of hemodynamic responses which recognize brain tasks toward developing a BCI. We applied two different mathematics tools separately, Wavelets analysis for preprocessing as signal filters and feature extractions and Neural networks for cognition brain tasks as a classification module. We also discuss and compare with other methods while our proposals perform better with an average accuracy of 99.9% for classification.

Keywords: Neural Networks, Neuroimaging, Wavelets, brain activity, functional near infrared spectroscope (fNIRs), braincomputer interface (BCI)

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1 Verification and Proposal of Information Processing Model Using EEG-Based Brain Activity Monitoring

Authors: Toshitaka Higashino, Naoki Wakamiya

Abstract:

Human beings perform a task by perceiving information from outside, recognizing them, and responding them. There have been various attempts to analyze and understand internal processes behind the reaction to a given stimulus by conducting psychological experiments and analysis from multiple perspectives. Among these, we focused on Model Human Processor (MHP). However, it was built based on psychological experiments and thus the relation with brain activity was unclear so far. To verify the validity of the MHP and propose our model from a viewpoint of neuroscience, EEG (Electroencephalography) measurements are performed during experiments in this study. More specifically, first, experiments were conducted where Latin alphabet characters were used as visual stimuli. In addition to response time, ERPs (event-related potentials) such as N100 and P300 were measured by using EEG. By comparing cycle time predicted by the MHP and latency of ERPs, it was found that N100, related to perception of stimuli, appeared at the end of the perceptual processor. Furthermore, by conducting an additional experiment, it was revealed that P300, related to decision making, appeared during the response decision process, not at the end. Second, by experiments using Japanese Hiragana characters, i.e. Japan's own phonetic symbols, those findings were confirmed. Finally, Japanese Kanji characters were used as more complicated visual stimuli. A Kanji character usually has several readings and several meanings. Despite the difference, a reading-related task and a meaning-related task exhibited similar results, meaning that they involved similar information processing processes of the brain. Based on those results, our model was proposed which reflects response time and ERP latency. It consists of three processors: the perception processor from an input of a stimulus to appearance of N100, the cognitive processor from N100 to P300, and the decision-action processor from P300 to response. Using our model, an application system which reflects brain activity can be established.

Keywords: eeg, information processing model, brain activity, model human processor

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