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Murat Akcakaya
University of Pittsburgh
88Publications
12H-index
448Citations
Publications 90
Newest
#1Aya Khalaf (University of Pittsburgh)H-Index: 4
#2Mohsen Nabian (NU: Northeastern University)H-Index: 4
Last.Sarah Ostadabbas (NU: Northeastern University)H-Index: 10
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Abstract Challenge and threat characterize distinct patterns of physiological response to a motivated performance task where the response patterns vary as a function of an individual's evaluation of task demands relative to his/her available resources to cope with the demands. Challenge and threat responses during motivated performance have been used to understand psychological, behavioral, and biological phenomena across many motivated performance domains. In this study, we aimed to investigate...
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#2Safaa EldeebH-Index: 1
Last.Deniz ErdogmusH-Index: 39
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We develop a dynamic system identification model to identify relationships among simultaneously recorded electroencephalography (EEG), electromyography (EMG) and force signals measured from 12 participants performing haptic interactions with 3D printed surfaces having different textures. In the first stage, we solve for the maximum likelihood (ML) parameter vector of a parsimonious integrated vector autoregression model (VAR) to estimate the latency between endogenous time variables, utilizing a...
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In this paper, we propose a machine learning approach for fault identification and protection of microgrid circuits. We model a representative microgrid architecture found in an industrial facility in simulation to motivate and demonstrate our approach. In particular, we formulated a 5-class classification problem to identify 5 different fault scenarios. Three-phase voltage and current waveforms corresponding to each fault type were analyzed using 5-level wavelet decomposition. Statistical featu...
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Modern approaches to providing haptic feedback focus mainly on robotic manipulators, vibrators, and tactors. This type of feedback tends to be cumbersome and limited to a small number of contact points. On the contrary, electrotactile displays are compact and wearable, and recent discoveries demonstrate that naturalistic sensations of touch can be provided by electrical stimulation of peripheral nerves. Haptic feedback is essential for daily activities, as it allows us to become aware of our sur...
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#1Aya KhalafH-Index: 4
#2Ervin SejdicH-Index: 26
Last.Murat AkcakayaH-Index: 12
view all 3 authors...
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#2Yijian XiangH-Index: 3
Last.Murat AkcakayaH-Index: 12
view all 6 authors...
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May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
#1Elise Dagois (University of Pittsburgh)
#2Aya Khalaf (University of Pittsburgh)H-Index: 4
Last.Murat Akcakaya (University of Pittsburgh)H-Index: 12
view all 4 authors...
In this paper, we introduce a transfer learning approach for our novel hybrid brain-computer interface in which electroencephalography and functional transcranial Doppler ultrasound are used simultaneously to record brain electrical activity and cerebral blood velocity respectively due to flickering mental rotation and word generation tasks. We reduced each trial into a scalar score using Regularized Discriminant Analysis (RDA). For each individual, class conditional probabilistic distribution o...
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May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
#1Yeganeh M. Marghi (NU: Northeastern University)H-Index: 2
#2Aziz Kocanaogullari (NU: Northeastern University)H-Index: 1
Last.Deniz Erdogmus (NU: Northeastern University)H-Index: 39
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In dynamic state-space models, the state can be estimated through recursive computation of the posterior distribution of the state given all measurements. In scenarios where active sensing/querying is possible, a hard decision is made when the state posterior achieves a pre-set confidence threshold. This mandate to meet a hard threshold may sometimes unnecessarily require more queries. In application domains where sensing/querying cost is of concern, some potential accuracy may be sacrificed for...
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#1Paula Gonzalez-Navarro (NU: Northeastern University)H-Index: 2
#2Yeganeh M. Marghi (NU: Northeastern University)H-Index: 2
Last.Deniz Erdogmus (NU: Northeastern University)H-Index: 39
view all 5 authors...
Electroencephalography (EEG) is an effective non-invasive measurement method to infer user intent in brain-computer interface (BCI) systems for control and communication, however, these systems often lack sufficient accuracy and speed due to low separability of class-conditional EEG feature distributions. Many factors impact system performance, including inadequate training datasets and models’ ignorance of the temporal dependency of brain responses to serial stimuli. Here, we propose a signal m...
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#1Aya Khalaf (University of Pittsburgh)H-Index: 4
#2Ervin Sejdic (University of Pittsburgh)H-Index: 26
Last.Murat Akcakaya (University of Pittsburgh)H-Index: 12
view all 3 authors...
Abstract Background Recently, hybrid brain-computer interfaces (BCIs) combining more than one modality have been investigated with the aim of boosting the performance of the existing single-modal BCIs in terms of accuracy and information transfer rate (ITR). Previously, we introduced a novel hybrid BCI in which EEG and fTCD modalities are used simultaneously to measure electrical brain activity and cerebral blood velocity during motor imagery (MI) tasks. New method In this paper, we used multi-s...
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