arXiv subjects

Seyed Kamaledin Setarehdan

Publications and source records attributed to Seyed Kamaledin Setarehdan.

4 recordsLinked to original sources

arXiv2025

Agency Perception and Brain Synchrony: A Hyperscanning Study of Human-Human and Human-AI Interaction

This study investigates how the human brain differentiates between intentional human agents and artificial intelligence (AI) agents during real-time social interaction. Using functional near-infrared spectroscopy (fNIRS) hyperscanning, we recorded prefrontal brain activity of participants as they played a one-on-one virtual tennis game, once against a human opponent and once against an AI-controlled opponent. Task-related cortical activation was evaluated using the General Linear Model (GLM), while inter-brain synchrony was assessed through Intersubject Correlation (ISC) analysis. Results revealed significantly stronger activation in the medial prefrontal cortex (mPFC) when participants interacted with human opponents, particularly in low-frequency hemodynamic bands (0.0098-0.0196 Hz). Moreover, neural synchrony between human dyads was significantly greater than in pseudo-paired controls, with distinct frequency-specific coupling patterns across channels. In contrast, AI interactions elicited reduced cortical engagement and no measurable inter-brain synchrony. These findings suggest that the perception of intentionality strongly modulates social brain responses, even when task structure and behavior remain constant. This work highlights the utility of fNIRS hyperscanning for studying naturalistic human interactions and provides neural evidence for the brain's sensitivity to perceived agency. The results have implications for the design of socially interactive AI systems that more effectively engage human cognition.

q-bio.NC↗

arXiv2025

Real-World fNIRS-Based Brain-Computer Interfaces: Benchmarking Deep Learning and Classical Models in Interactive Gaming

Brain-Computer Interfaces enable direct communication between the brain and external systems, with functional Near-Infrared Spectroscopy emerging as a portable and non-invasive method for capturing cerebral hemodynamics. This study investigates the classification of rest and task states during a realistic, interactive tennis simulation using fNIRS signals and a range of machine learning approaches. We benchmarked traditional classifiers based on engineered features, Long Short-Term Memory networks on raw time-series data, and Convolutional Neural Networks applied to Gramian Angular Field-transformed images. Ensemble models like Extra Trees and Gradient Boosting achieved accuracies above 97 percent, while the ResNet-based CNN reached 95.0 percent accuracy with a near-perfect AUC of 99.2 percent, outperforming both LSTM and EfficientNet architectures. A novel data augmentation strategy was employed to equalize trial durations while preserving physiological integrity. Feature importance analyses revealed that both oxygenated and deoxygenated hemoglobin signals, particularly slope and RMS metrics, were key contributors to classification performance. These findings demonstrate the strong potential of fNIRS-based BCIs for deployment in dynamic, real-world environments and underscore the advantages of deep learning models in decoding complex neural signals.

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arXiv2021

Classification of Breast Cancer Lesions in Ultrasound Images by using Attention Layer and loss Ensembles in Deep Convolutional Neural Networks

Reliable classification of benign and malignant lesions in breast ultrasound images can provide an effective and relatively low cost method for early diagnosis of breast cancer. The accuracy of the diagnosis is however highly dependent on the quality of the ultrasound systems and the experience of the users (radiologists). The leverage in deep convolutional neural network approaches provided solutions in efficient analysis of breast ultrasound images. In this study, we proposed a new framework for classification of breast cancer lesions by use of an attention module in modified VGG16 architecture. We also proposed new ensembled loss function which is the combination of binary cross-entropy and logarithm of the hyperbolic cosine loss to improve the model discrepancy between classified lesions and its labels. Networks trained from pretrained ImageNet weights, and subsequently fine-tuned with ultrasound datasets. The proposed model in this study outperformed other modified VGG16 architectures with the accuracy of 93% and also the results are competitive with other state of the art frameworks for classification of breast cancer lesions. In this study, we employed transfer learning approaches with the pre-trained VGG16 architecture. Different CNN models for classification task were trained to predict benign or malignant lesions in breast ultrasound images. Our Experimental results show that the choice of loss function is highly important in classification task and by adding an attention block we could empower the performance our model.

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arXiv2020

Accurate Stress Assessment based on functional Near Infrared Spectroscopy using Deep Learning Approach

Stress is known as one of the major factors threatening human health. A large number of studies have been performed in order to either assess or relieve stress by analyzing the brain and heart-related signals. In this study, signals produced by functional Near-Infrared Spectroscopy (fNIRS) of the brain recorded from 10 healthy volunteers are employed to assess the stress induced by the Montreal Imaging Stress Task by means of a deep learning system. The proposed deep learning system consists of two main parts: First, the one-dimensional convolutional neural network is employed to build informative feature maps. Then, a stack of deep fully connected layers is used to predict the stress existence probability. Experiment results showed that the trained fNIRS model performs stress classification by achieving 88.52 -+ 0.77% accuracy. Employment of the proposed deep learning system trained on the fNIRS measurements leads to higher stress classification accuracy than the existing methods proposed in fNIRS studies in which the same experimental procedure has been employed. The proposed method suggests better stability with lower variation in prediction. Furthermore, its low computational cost opens up the possibility to be applied in real-time stress assessment.

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