Dr. Aykut Eken Profile

Dr. Aykut Eken

at TOBB ETÜ

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Publications (3)

SPIE Journal Paper | 17 December 2024 Open Access

Explainable fNIRS-based pain decoding under pharmacological conditions via deep transfer learning approach

Aykut Eken, Murat Yüce, Gülnaz Yükselen, Sinem Erdogan

NPh, Vol. 11, Issue 04, 045015, (December 2024) https://doi.org/10.1117/12.10.1117/1.NPh.11.4.045015

KEYWORDS: Data modeling, Performance modeling, Education and training, Hemodynamics, Staring arrays, Neurophotonics, Deep learning, Statistical modeling, Brain, Design

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SignificanceAssessment of pain and its clinical diagnosis rely on subjective methods which become even more complicated under analgesic drug administrations.AimWe aim to propose a deep learning (DL)–based transfer learning (TL) methodology for objective classification of functional near-infrared spectroscopy (fNIRS)–derived cortical oxygenated hemoglobin responses to painful and non-painful stimuli presented under different timings post-analgesic and placebo drug administration.ApproachA publicly available fNIRS dataset obtained during painful/non-painful stimuli was used. Separate fNIRS scans were taken under the same protocol before drug (morphine and placebo) administration and at three different timings (30, 60, and 90 min) post-administration. Data from pre-drug fNIRS scans were utilized for constructing a base DL model. Knowledge generated from the pre-drug model was transferred to six distinct post-drug conditions by following a TL approach. The DeepSHAP method was utilized to unveil the contribution weights of nine regions of interest for each of the pre-drug and post-drug decoding models.ResultsAccuracy, sensitivity, specificity, and area under curve (AUC) metrics of the pre-drug model were above 90%, whereas each of the post-drug models demonstrated a performance above 90% for the same metrics. Post-placebo models had higher decoding accuracy than post-morphine models. Knowledge obtained from a pre-drug base model could be successfully utilized to build pain decoding models for six distinct brain states that were scanned at three different timings after either analgesic or placebo drug administration. The contribution of different cortical regions to classification performance varied across the post-drug models.ConclusionsThe proposed DL-based TL methodology may remove the necessity to build DL models for data collected at clinical or daily life conditions for which obtaining training data is not practical or building a new decoding model will have a computational cost. Unveiling the explanation power of different cortical regions may aid the design of more computationally efficient fNIRS-based brain–computer interface (BCI) system designs that target other application areas.

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Proceedings Article | 5 March 2021 Presentation

Non-invasive estimation of intracranial pressure by fast diffuse correlation spectroscopy: a multi-center study

Jonas Fischer, Susanna Tagliabue, Federica Maruccia, Amelia Jiménez-Sánchez, Eashani Sathialingam, Wesley Baker, Aykut Eken, Ameer Ghouse, Anna Rey-Perez, Marcelino Báguena, Katiuska Rosas, Ofer Sadan, Prem Kandiah, Owen Samuels, Ramani Balu, Riccardo Zucca, Udo Weigel, David Busch, Erin Buckley, Arjun Yodh, Daniel Licht, W. Andrew Kofke, Maria Poca, Gemma Piella, Juan Sahuquillo, Turgut Durduran

Proceedings Volume 11639, 1163912 (2021) https://doi.org/10.1117/12.2577561

KEYWORDS: Spectroscopy, Data modeling, Traumatic brain injury, Statistical analysis, Optical testing, Neural networks, Machine learning, Data analysis, Blood circulation

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SPIE Journal Paper | 5 September 2017 Open Access

Differential efficiency of transcutaneous electrical nerve stimulation in dominant versus nondominant hands in fibromyalgia: placebo-controlled functional near-infrared spectroscopy study

Aykut Eken, Murat Kara, Bora Baskak, Aysegül Baltaci, Didem Gökcay

NPh, Vol. 5, Issue 01, 011005, (September 2017) https://doi.org/10.1117/12.10.1117/1.NPh.5.1.011005

KEYWORDS: Fermium, Frequency modulation, Nerve, Control systems, Brain, Hemodynamics, Neurophotonics, Near infrared spectroscopy, Silver, Neuroimaging

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