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Magnetic Resonance Imaging (MRI) reconstruction is computationally heavy from under sampled data, and centralized data sharing within deep learning models was met with privacy concerns. We therefore propose FL-Swarm MRCM, a novel federated learning framework that integrates FedDyn dynamic regularization, a swarm-optimized generative adversarial network (SwarmGAN), and a structure-aware cross-entropy loss to enhance cross-site MRI reconstruction without sharing raw data. The framework avoids client drift, locally adapts hyper-parameters using Particle Swarm Optimization, and preserves anatomic fidelity. Evaluations on fastMRI, BraTS-2020, and OASIS datasets under non-IID partitions show that FL-Swarm MRCM improves reconstruction quality, achieving PSNR = 29.78 dB and SSIM = 0.984, outscoring FL-MR and FL-MRCM baselines. The federated framework for adversarial training proposed here enables reproducible, privacy-preserving, and strongly multi-institutional MRI reconstruction with better cross-site generalization for clinical use.

The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering have made medical image analysis one of the top research and development area. One of the reasons for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This includes application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.

The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart’s rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients’ acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.

Emotion recognition has increased the potential of affective computing by getting an instant feedback from users and thereby, have a better understanding of their behavior. Physiological sensors have been used to recognize human emotions in response to audio and video content that engages single (auditory) and multiple (two: auditory and vision) human senses, respectively. In this study, human emotions were recognized using physiological signals observed in response to tactile enhanced multimedia content that engages three (tactile, vision, and auditory) human senses. The aim was to give users an enhanced real-world sensation while engaging with multimedia content. To this end, four videos were selected and synchronized with an electric fan and a heater, based on timestamps within the scenes, to generate tactile enhanced content with cold and hot air effect respectively. Physiological signals, i.e., electroencephalography (EEG), photoplethysmography (PPG), and galvanic skin response (GSR) were recorded using commercially available sensors, while experiencing these tactile enhanced videos. The precision of the acquired physiological signals (including EEG, PPG, and GSR) is enhanced using pre-processing with a Savitzky-Golay smoothing filter. Frequency domain features (rational asymmetry, differential asymmetry, and correlation) from EEG, time domain features (variance, entropy, kurtosis, and skewness) from GSR, heart rate and heart rate variability from PPG data are extracted. The K nearest neighbor classifier is applied to the extracted features to classify four (happy, relaxed, angry, and sad) emotions. Our experimental results show that among individual modalities, PPG-based features gives the highest accuracy of 78.57 % as compared to EEG- and GSR-based features. The fusion of EEG, GSR, and PPG features further improved the classification accuracy to 79.76 % (for four emotions) when interacting with tactile enhanced multimedia.

Stress research is a rapidly emerging area in the field of electroencephalography (EEG) signal processing. The use of EEG as an objective measure for cost effective and personalized stress management becomes important in situations like the nonavailability of mental health facilities. In this study, long-term stress was classified with machine learning algorithms using resting state EEG signal recordings. The labeling for the stress and control groups was performed using two currently accepted clinical practices: (i) the perceived stress scale score and (ii) expert evaluation. The frequency domain features were extracted from five-channel EEG recordings in addition to the frontal and temporal alpha and beta asymmetries. The alpha asymmetry was computed from four channels and used as a feature. Feature selection was also performed to identify statistically significant features for both stress and control groups (via t-test). We found that support vector machine was best suited to classify long-term human stress when used with alpha asymmetry as a feature. It was observed that the expert evaluation-based labeling method had improved the classification accuracy by up to 85.20%. Based on these results, it is concluded that alpha asymmetry may be used as a potential bio-marker for stress classification, when labels are assigned using expert evaluation.

Video games have become a ubiquitous part of demographically diverse cultures. Numerous studies have focused on analyzing the cognitive aspects involved in game playing that could help in providing an optimal gaming experience by improving video game design. To this end, we present a framework for classifying the game player’s expertise level using wearable electroencephalography (EEG) headset. We hypothesize that expert and novice players’ brain activity is different, which can be classified using frequency domain features extracted from EEG signals of the game player. A systematic channel reduction approach is presented using a correlation-based attribute evaluation method. This approach lead us in identifying two significant EEG channels, i.e., AF3 and P7, among fourteen channels available in Emotiv EPOC headset. In particular, features extracted from these two EEG channels contributed the most to the video game player’s expertise level classification. This finding is validated by performing statistical analysis (t-test) over the extracted features. Moreover, among multiple classifiers used, K-nearest neighbor is the best classifier in classifying game player’s expertise level with a classification accuracy of up to 98.04% (without data balancing) and 98.33% (with data balancing).

Mental disorders represent a critical global health challenge that affects millions around the world and significantly disrupts daily life. Early and accurate detection is paramount for timely intervention, which can lead to improved treatment outcomes. Electroencephalography (EEG) provides the non-invasive means for observing brain activity, making it a useful tool for detecting potential mental disorders. Recently, deep learning techniques have gained prominence for their ability to analyze complex datasets, such as electroencephalography recordings. In this study, we introduce a novel deep-learning architecture for the classification of mental disorders such as post-traumatic stress disorder, depression, or anxiety, using electroencephalography data. Our proposed model, the multichannel convolutional transformer, integrates the strengths of both convolutional neural networks and transformers. Before feeding the model as low-level features, the input is pre-processed using a common spatial pattern filter, a signal space projection filter, and a wavelet denoising filter. Then the EEG signals are transformed using continuous wavelet transform to obtain a time-frequency representation. The convolutional layers tokenize the input signals transformed by our pre-processing pipeline, while the Transformer encoder effectively captures long-range temporal dependencies across sequences. This architecture is specifically tailored to process EEG data that has been preprocessed using continuous wavelet transform, a technique that provides a time-frequency representation, thereby enhancing the extraction of relevant features for classification. We evaluated the performance of our proposed model on three datasets: the EEG Psychiatric Dataset, the MODMA dataset, and the EEG and Psychological Assessment dataset. Our model achieved classification accuracies of 87.40% on the EEG and Psychological Assessment dataset, 89.84% on the MODMA dataset, and 92.28% on the EEG Psychiatric dataset. Our approach outperforms every concurrent approaches on the datasets we used, without showing any sign of over-fitting. These results underscore the potential of our proposed architecture in delivering accurate and reliable mental disorder detection through EEG analysis, paving the way for advancements in early diagnosis and treatment strategies.

Humans use facial expressions to convey personal feelings. Facial expressions need to be automatically recognized to design control and interactive applications. Feature extraction in an accurate manner is one of the key steps in automatic facial expression recognition system. Current frequency domain facial expression recognition systems have not fully utilized the facial elements and muscle movements for recognition. In this paper, stationary wavelet transform is used to extract features for facial expression recognition due to its good localization characteristics, in both spectral and spatial domains. More specifically a combination of horizontal and vertical subbands of stationary wavelet transform is used as these subbands contain muscle movement information for majority of the facial expressions. Feature dimensionality is further reduced by applying discrete cosine transform on these subbands. The selected features are then passed into feed forward neural network that is trained through back propagation algorithm. An average recognition rate of 98.83% and 96.61% is achieved for JAFFE and CK+ dataset, respectively. An accuracy of 94.28% is achieved for MS-Kinect dataset that is locally recorded. It has been observed that the proposed technique is very promising for facial expression recognition when compared to other state-of-the-art techniques.

The goal of this study is to enhance the emotional experience of a viewer by using enriched multimedia content, which entices tactile sensation in addition to vision and auditory senses. A user-independent method of emotion recognition using electroencephalography (EEG) in response to tactile enhanced multimedia (TEM) is presented with an aim of enriching the human experience of viewing digital content. The selected traditional multimedia clips are converted into TEM clips by synchronizing them with an electric fan and a heater to add cold and hot air effect. This would give realistic feel to a viewer by engaging three human senses including vision, auditory, and tactile. The EEG data is recorded from 21 participants in response to traditional multimedia clips and their TEM versions. Self assessment manikin (SAM) scale is used to collect valence and arousal score in response to each clip to validate the evoked emotions. A t-test is applied on the valence and arousal values to measure any significant difference between multimedia and TEM clips. The resulting p-values show that traditional multimedia and TEM content are significantly different in terms of valence and arousal scores, which shows TEM clips have enhanced evoked emotions. For emotion recognition, twelve time domain features are extracted from the preprocessed EEG signal and a support vector machine is applied to classify four human emotions i.e., happy, angry, sad, and relaxed. An accuracy of 43.90% and 63.41% against traditional multimedia and TEM clips is achieved respectively, which shows that EEG based emotion recognition performs better by engaging tactile sense.

A study on classification of psychological stress in humans using electroencephalography (EEG) is presented. The stress is classified using a correlation-based feature subset selection method that efficiently reduces the feature vector length. In this study, twenty-eight participants are involved by filling in the perceived stress scale-10 (PSS-10) questionnaire and their EEG is also recorded in closed eye condition to measure the baseline stress. The recorded data is labelled on the basis of the stress level that is indicated by the participant’s PSS score. The feature selection method has shown that, among the EEG oscillations, low beta, high beta, and low gamma are the most significant neural oscillations for classifying human stress. The proposed method not only reduces the time to build a classification model but also improves the classification accuracy up to 78.57% using a single channel wearable EEG device.