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Murat Akcakaya
University of Pittsburgh
82Publications
10H-index
350Citations
Publications 82
Newest
Aya Khalaf3
Estimated H-index: 3
(University of Pittsburgh),
Mohsen Nabian2
Estimated H-index: 2
(NU: Northeastern University)
+ -3 AuthorsChun-An Chou10
Estimated H-index: 10
(NU: Northeastern University)
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...
Published on Jun 1, 2019in Journal of Neural Engineering 4.55
Aya Khalaf3
Estimated H-index: 3
,
Ervin Sejdić24
Estimated H-index: 24
,
Murat Akcakaya10
Estimated H-index: 10
Published on May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
Elise Dagois (University of Pittsburgh), Aya Khalaf3
Estimated H-index: 3
(University of Pittsburgh)
+ 1 AuthorsMurat Akcakaya10
Estimated H-index: 10
(University of Pittsburgh)
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...
Published on May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
Yeganeh M. Marghi2
Estimated H-index: 2
(NU: Northeastern University),
Aziz Kocanaogullari (NU: Northeastern University)+ 2 AuthorsDeniz Erdomus
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...
Paula Gonzalez-Navarro2
Estimated H-index: 2
(NU: Northeastern University),
Yeganeh M. Marghi2
Estimated H-index: 2
(NU: Northeastern University)
+ 2 AuthorsDeniz Erdogmus38
Estimated H-index: 38
(NU: Northeastern University)
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...
Published on May 1, 2019in Journal of Neuroscience Methods 2.79
Aya Khalaf3
Estimated H-index: 3
(University of Pittsburgh),
Ervin Sejdić24
Estimated H-index: 24
(University of Pittsburgh),
Murat Akcakaya10
Estimated H-index: 10
(University of Pittsburgh)
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...
Published on Mar 1, 2019in Biomedical Signal Processing and Control 2.94
Safaa Eldeeb1
Estimated H-index: 1
(University of Pittsburgh),
Murat Akcakaya10
Estimated H-index: 10
(University of Pittsburgh)
+ 4 AuthorsAmit Sethi5
Estimated H-index: 5
(University of Pittsburgh)
Abstract In this paper, we introduce electroencephalography (EEG)- PDC based network connectivity average mean degrees (E-PDC) measure to analyze the interhemispheric interaction between the left and right motor cortices after stroke. E-PDC uses a graph and partial directed coherence (PDC) approach to quantify the directional functional connectivity between the motor cortices, which is not only altered after stroke but also is one of the important mechanisms linked with poor recovery of hand fun...
Published on Mar 1, 2019
Aya Khalaf3
Estimated H-index: 3
(University of Pittsburgh),
Ervin Sejdić24
Estimated H-index: 24
(University of Pittsburgh),
Murat Akcakaya10
Estimated H-index: 10
(University of Pittsburgh)
Published on Feb 1, 2019in Signal Processing 4.09
Yijian Xiang3
Estimated H-index: 3
(WashU: Washington University in St. Louis),
Murat Akcakaya10
Estimated H-index: 10
(University of Pittsburgh)
+ 2 AuthorsArye Nehorai56
Estimated H-index: 56
(WashU: Washington University in St. Louis)
Abstract To cope with complicated environments and stealthier targets, incorporating intelligence and cognition cycles into target tracking is of great importance in modern sensor network management. With remarkable advances in sensor techniques and deployable platforms, a sensing system has freedom to select a subset of available radars, plan their trajectories, and transmit designed waveforms. In this paper, we propose a general framework for single target tracking in cognitive networks of rad...
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