فهرست مطالب zahra shirzhiyan

Air traffic control is a very complex process, including multiple human-machine interactions. Human mental workload plays an important role in this process. Nowadays, electroencephalography indexes are considered as new indicators in the field of assessment of mental workload. The purpose of the present study was to investigate the relationship between EEG theta power and mental workload in air traffic control simulation.

Fourteen air traffic controllers participated in this study. Controllers carried out two scenarios, including low and high workload, based on task load factors in an air traffic control simulator. Mental workload was assessed in these two scenarios by the NASA-TLX questionnaire. EEG signals were continuously recorded during air traffic control tasks. Afterward, absolute theta power was extracted from participants’ EEG using Fast Fourier Transform (FFT) by the MATLAB software and was compared with each other in terms of high and low workload.

The results showed a significant relationship in absolute theta power during low and high workload scenarios in all regions of the brain (p < 0.05). Absolute theta power increased primarily in the frontal region during the high workload scenario. Also, there was a significant increase in the relationship between work experience and absolute theta power at the F3 region during the high workload scenario (P=0.021, r=0.607).

Absolute theta power provides a good parameter to assess mental workload at different levels of air traffic control tasks. Therefore, it can be used as a tool for the design of human-machine complex systems.

Many studies have suggested that Cochlear Implant (CI) users vary in terms of speech recognition in noise. Studies in this field attribute this variety partly to subcortical auditory processing. Since study on speech-Auditory Brainstem Response (speech-ABR) provides good information about speech processing, so this work was designed to compare speech-ABR components between two groups of CI users with good and poor speech recognition in noise scores.

The present study was conducted on two groups of CI users aged 8-10 years old. The first group (CI-good) consisted of 15 children prelingual CI users who had good speech recognition in noise performance. The second group (CI-poor) matched with the first group, but they had poor speech recognition in noise performance. The speech-ABR test in a sound-field presentation was performed for all the participants.

The speech-ABR response showed more delay in C, D, E, F, O latencies in CI-poor than CI-good users (P <0.05), meanwhile no significant difference was observed in initial wave (V(t= -0.293, p= 0.771 and A(t= -1.051, p= 0.307). Analysis in spectral-domain showed a weaker representation of fundamental frequency as well as the first formant and high-frequency component of speech stimuli in the CI-poor users.

Results revealed that CI users who showed poor auditory performance in noise performance had deficits in encoding of periodic portion of speech signals at brainstem level. Also, this study could be as physiological evidence for poorer pitch processing in CI users with poor speech recognition in noise performance.

Many of the current brain-computer interface systems rely on the patient`s ability to control voluntary eye movements. Some diseases can lead to defects in the visual system. Due to the intactness of these patients` auditory system, researchers moved towards the auditory paradigms. Attention can modulate the power of auditory steady-state response. As a result, this response is useful in an auditory brain-computer interface.
As humans intrinsically enjoy listening to rhythmic sounds, this project was carried out with the aim of extraction and classification of the EEG signal patterns in response to simple and rhythmic auditory stimuli to investigate the possibility of using the rhythmic stimuli in brain-computer interface systems.
Two three-membered simple and rhythmic groups of auditory sinusoidally amplitude-modulated tones were generated as the stimuli. Corresponding EEG signals were recorded and classified by means of five-fold cross-validated naïve Bayes classifier on the basis of power spectral density at message frequencies.
There was no significant difference between the classification performances of the responses to each group of the stimuli. All the classification accuracies, even without any noise reduction and artifact rejection, was greater than the acceptable value for being used in brain-computer interface systems (70%).
Like the common sinusoidally amplitude-modulated tones, the novel proposed rhythmic stimuli in this project have a promising discrimination for being used in brain-computer interface systems. In addition, Power spectral density has provided an appropriate discrimination for within- and between-subject EEG classification.

In elementary studies on brainstem evoked potentials a simple stimuli likeclick and sinusoidal tones is used, but in recent years Auditory Neuroscience orientedto use complex stimuli. These complex stimuli (e.g. speech and music) are morecapable in representation of auditory pathway functions. Previous studies in this field,mainly attend to one single vowel or consonant-vowels. Until now no study has beendone which considered the encoding of multi structurally meaning full combination ofconsonant-vowel. In this study, we try to extract information using suitable tools fromAuditory Brainstem Responses (ABR) to stimuli ‘’baba’’. At the first step we used a test to find an appropriate distance betweentwo consecutive consonant- vowels ‘ba’ which is perceived ‘baba’. For this, apsychophysical test was designed. Subjects were asked to choose a suitable distancebetween two ‘ba’ that the combination perceived ‘baba’. After recording evokedpotentials to ‘ba’ and ‘baba’, we searched distinctive features between the signalsrelated two stimuli. So at first, we began with comparative time-frequency analyseslike correlation and coherence. Correlation analyses show that the response to ‘ba’ and the response to firstsyllable of ‘baba’ in the Onset and also transient parts of responses are different and theresponse to first and second syllable of /baba/ become similar. The results of coherenceanalyses show that these differences could not be represented with a linear relationmerely. Brainstem neural activity was different in countering with single syllablestimuli in comparison with meaningful disyllabic stimuli. These changes can beconsequences of activities in anatomical top-down pathway.

Gathering an insight into brainstem task in generating auditory response to complex stimuli and its nonlinear behavior can be an important base in auditory system modelling, but no study has been done to demonstrate the nonlinear dynamic behavior of auditory systems considering cABR. This study attends the dynamic modeling of auditory brainstem response to consonant-vowel syllable /da/ using fuzzy logic as nonlinear mapping of the input and output of the system. We recorded cABR to /da/ from 40 normal Farsi speaking subjects in response to /da/ with 40ms duration. This data set was divided to train and validation sets. We implemented a fuzzy logic based model for the dynamic extraction of cABR to /da/ for data set. This model includes singltone fuzzifier, product inference engine and weighted center of average defuzzifier. Rule base representing dynamic of signal was generated and, then, firing rate of each rule was calculated and a histogram of rule firing rate was plotted. We selected the important regions of the histogram regarding to firing pattern of the rule. By choosing an appropriate threshold, a secondary rule reduction was done to generate a simplified model; remaining rules were best rules related to important cues of cABR. This model represents the input-output behavior of the brainstem in generating cABR to consonant-vowel /da/. The total error achieved by cross-validation of the model after an important rule selection is 0.1329 with a variance of 7.08×10-4. Nonlinear fuzzy based dynamic extraction of cABR signal is a valid approach for generating important features of cABR and a remarkable evidence of these signals can be represented by some spatial rules.