Tag: eess.SP

Monitoring Wandering Behavior of Persons Suffering from Dementia Using BLE Based Localization System

Marcin Kolakowski, Bartosz Blachucki Edit "Monitoring Wandering Behavior of Persons Suffering from Dementia Using BLE Based Localization System"
arXiv:2403.19704v1 Announce Type: new Abstract: With the aging of our populations, dementia will become a problem which would directly or indirectly affect a large number of people. One of the most dangerous dementia symptoms is wandering. It consists in aimless walking and spatial disorientation, which might lead to various unpleasant situations like falling down accidents at home to leaving the living place and going missing. Therefore, in order to ensure elderly people's safety it is crucial to detect and alarm the caregivers in case of such incidents. It can be done by tracking the sufferers movements and detecting signs of repetitiveness. The paper presents the results of the study, in which the wandering behavior of people suffering from dementia was monitored using a Bluetooth Low Energy based positioning system. The paper includes the description of the system used for patients localization and the results of the tests performed in a long term care facility.

D$^2$-JSCC: Digital Deep Joint Source-channel Coding for Semantic Communications

Jianhao Huang, Kai Yuan, Chuan Huang, Kaibin Huang Edit "D$^2$-JSCC: Digital Deep Joint Source-channel Coding for Semantic Communications"
arXiv:2403.07338v1 Announce Type: cross Abstract: Semantic communications (SemCom) have emerged as a new paradigm for supporting sixth-generation applications, where semantic features of data are transmitted using artificial intelligence algorithms to attain high communication efficiencies. Most existing SemCom techniques utilize deep neural networks (DNNs) to implement analog source-channel mappings, which are incompatible with existing digital communication architectures. To address this issue, this paper proposes a novel framework of digital deep joint source-channel coding (D$^2$-JSCC) targeting image transmission in SemCom. The framework features digital source and channel codings that are jointly optimized to reduce the end-to-end (E2E) distortion. First, deep source coding with an adaptive density model is designed to encode semantic features according to their distributions. Second, digital channel coding is employed to protect encoded features against channel distortion. To facilitate their joint design, the E2E distortion is characterized as a function of the source and channel rates via the analysis of the Bayesian model and Lipschitz assumption on the DNNs. Then to minimize the E2E distortion, a two-step algorithm is proposed to control the source-channel rates for a given channel signal-to-noise ratio. Simulation results reveal that the proposed framework outperforms classic deep JSCC and mitigates the cliff and leveling-off effects, which commonly exist for separation-based approaches.

High-speed Low-consumption sEMG-based Transient-state micro-Gesture Recognition by Spiking Neural Network

Youfang Han, Wei Zhao, Xiangjin Chen, Xin Meng Edit "High-speed Low-consumption sEMG-based Transient-state micro-Gesture Recognition by Spiking Neural Network"
arXiv:2403.06998v1 Announce Type: cross Abstract: Gesture recognition on wearable devices is extensively applied in human-computer interaction. Electromyography (EMG) has been used in many gesture recognition systems for its rapid perception of muscle signals. However, analyzing EMG signals on devices, like smart wristbands, usually needs inference models to have high performances, such as low inference latency, low power consumption, and low memory occupation. Therefore, this paper proposes an improved spiking neural network (SNN) to achieve these goals. We propose an adaptive multi-delta coding as a spiking coding method to improve recognition accuracy. We propose two additive solvers for SNN, which can reduce inference energy consumption and amount of parameters significantly, and improve the robustness of temporal differences. In addition, we propose a linear action detection method TAD-LIF, which is suitable for SNNs. TAD-LIF is an improved LIF neuron that can detect transient-state gestures quickly and accurately. We collected two datasets from 20 subjects including 6 micro gestures. The collection devices are two designed lightweight consumer-level EMG wristbands (3 and 8 electrode channels respectively). Compared to CNN, FCN, and normal SNN-based methods, the proposed SNN has higher recognition accuracy. The accuracy of the proposed SNN is 83.85% and 93.52% on the two datasets respectively. In addition, the inference latency of the proposed SNN is about 1% of CNN, the power consumption is about 0.1% of CNN, and the memory occupation is about 20% of CNN. The proposed methods can be used for precise, high-speed, and low-power micro-gesture recognition tasks, and are suitable for consumer-level intelligent wearable devices, which is a general way to achieve ubiquitous computing.

Investigating the Generalizability of Physiological Characteristics of Anxiety

Emily Zhou, Mohammad Soleymani, Maja J. Matari\'c Edit "Investigating the Generalizability of Physiological Characteristics of Anxiety"
arXiv:2402.15513v1 Announce Type: new Abstract: Recent works have demonstrated the effectiveness of machine learning (ML) techniques in detecting anxiety and stress using physiological signals, but it is unclear whether ML models are learning physiological features specific to stress. To address this ambiguity, we evaluated the generalizability of physiological features that have been shown to be correlated with anxiety and stress to high-arousal emotions. Specifically, we examine features extracted from electrocardiogram (ECG) and electrodermal (EDA) signals from the following three datasets: Anxiety Phases Dataset (APD), Wearable Stress and Affect Detection (WESAD), and the Continuously Annotated Signals of Emotion (CASE) dataset. We aim to understand whether these features are specific to anxiety or general to other high-arousal emotions through a statistical regression analysis, in addition to a within-corpus, cross-corpus, and leave-one-corpus-out cross-validation across instances of stress and arousal. We used the following classifiers: Support Vector Machines, LightGBM, Random Forest, XGBoost, and an ensemble of the aforementioned models. We found that models trained on an arousal dataset perform relatively well on a previously unseen stress dataset, and vice versa. Our experimental results suggest that the evaluated models may be identifying emotional arousal instead of stress. This work is the first cross-corpus evaluation across stress and arousal from ECG and EDA signals, contributing new findings about the generalizability of stress detection.

Generating Visual Stimuli from EEG Recordings using Transformer-encoder based EEG encoder and GAN

Rahul Mishra, Arnav Bhavsar Edit "Generating Visual Stimuli from EEG Recordings using Transformer-encoder based EEG encoder and GAN"
arXiv:2402.10115v1 Announce Type: cross Abstract: In this study, we tackle a modern research challenge within the field of perceptual brain decoding, which revolves around synthesizing images from EEG signals using an adversarial deep learning framework. The specific objective is to recreate images belonging to various object categories by leveraging EEG recordings obtained while subjects view those images. To achieve this, we employ a Transformer-encoder based EEG encoder to produce EEG encodings, which serve as inputs to the generator component of the GAN network. Alongside the adversarial loss, we also incorporate perceptual loss to enhance the quality of the generated images.

Wavelet Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types

Ali Rabiee, Sima Ghafoori, Anna Cetera, Reza Abiri Edit "Wavelet Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types"
arXiv:2402.09447v1 Announce Type: cross Abstract: This research aims to decode hand grasps from Electroencephalograms (EEGs) for dexterous neuroprosthetic development and Brain-Computer Interface (BCI) applications, especially for patients with motor disorders. Particularly, it focuses on distinguishing two complex natural power and precision grasps in addition to a neutral condition as a no-movement condition using a new EEG-based BCI platform and wavelet signal processing. Wavelet analysis involved generating time-frequency and topographic maps from wavelet power coefficients. Then, by using machine learning techniques with novel wavelet features, we achieved high average accuracies: 85.16% for multiclass, 95.37% for No-Movement vs Power, 95.40% for No-Movement vs Precision, and 88.07% for Power vs Precision, demonstrating the effectiveness of these features in EEG-based grasp differentiation. In contrast to previous studies, a critical part of our study was permutation feature importance analysis, which highlighted key features for grasp classification. It revealed that the most crucial brain activities during grasping occur in the motor cortex, within the alpha and beta frequency bands. These insights demonstrate the potential of wavelet features in real-time neuroprosthetic technology and BCI applications.

A Physiological Sensor-Based Android Application Synchronized with a Driving Simulator for Driver Monitoring

cs.HC updates on arXiv.org Edit "A Physiological Sensor-Based Android Application Synchronized with a Driving Simulator for Driver Monitoring"
In this paper, we present an Android application to control and monitor the physiological sensors from the Shimmer platform and its synchronized working with a driving simulator. The Android app can monitor drivers and their parameters can be used to analyze the relation between their physiological states and driving performance. The app can configure, select, receive, process, represent graphically, and store the signals from electrocardiogram (ECG), electromyogram (EMG) and galvanic skin response (GSR) modules and accelerometers, a magnetometer and a gyroscope. The Android app is synchronized in two steps with a driving simulator that we previously developed using the Unity game engine to analyze driving security and efficiency. The Android app was tested with different sensors working simultaneously at various sampling rates and in different Android devices. We also tested the synchronized working of the driving simulator and the Android app with 25 people and analyzed the relation between data from the ECG, EMG, GSR, and gyroscope sensors and from the simulator. Among others, some significant correlations between a gyroscope-based feature calculated by the Android app and vehicle data and particular traffic offences were found. The Android app can be applied with minor adaptations to other different users such as patients with chronic diseases or athletes.

Semantic segmentation for recognition of epileptiform patterns recorded via Microelectrode Arrays in vitro

q-bio.NC updates on arXiv.org Edit "Semantic segmentation for recognition of epileptiform patterns recorded via Microelectrode Arrays in vitro"
Epilepsy is a prevalent neurological disorder that affects approximately 1% of the global population. Around 30-40% of patients do not respond to pharmacological treatment, leading to a significant negative impact on their quality of life. Closed-loop deep brain stimulation (DBS) is a promising treatment for individuals who do not respond to medical therapy. To achieve effective seizure control, algorithms play an important role in identifying relevant electrographic biomarkers from local field potentials (LFPs) to determine the optimal stimulation timing. In this regard, the detection and classification of events from ongoing brain activity, while achieving low power through computationally unexpensive implementations, represents a major challenge in the field. To address this challenge, we here present two lightweight algorithms, the ZdensityRODE and the AMPDE, for identifying relevant events from LFPs by utilizing semantic segmentation, which involves extracting different levels of information from the LFP and relevant events from it. The algorithms performance was validated against epileptiform activity induced by 4-minopyridine in mouse hippocampus-cortex (CTX) slices and recorded via microelectrode array, as a case study. The ZdensityRODE algorithm showcased a precision and recall of 93% for ictal event detection and 42% precision for interictal event detection, while the AMPDE algorithm attained a precision of 96% and recall of 90% for ictal event detection and 54% precision for interictal event detection. While initially trained specifically for detection of ictal activity, these algorithms can be fine-tuned for improved interictal detection, aiming at seizure prediction. Our results suggest that these algorithms can effectively capture epileptiform activity; their light weight opens new possibilities for real-time seizure detection and seizure prediction and control.

On Leaky-Integrate-and Fire as Spike-Train-Quantization Operator on Dirac-Superimposed Continuous-Time Signals

cs.NE updates on arXiv.org Edit "On Leaky-Integrate-and Fire as Spike-Train-Quantization Operator on Dirac-Superimposed Continuous-Time Signals"
Leaky-integrate-and-fire (LIF) is studied as a non-linear operator that maps an integrable signal $f$ to a sequence $\eta_f$ of discrete events, the spikes. In the case without any Dirac pulses in the input, it makes no difference whether to set the neuron's potential to zero or to subtract the threshold $\vartheta$ immediately after a spike triggering event. However, in the case of superimpose Dirac pulses the situation is different which raises the question of a mathematical justification of each of the proposed reset variants. In the limit case of zero refractory time the standard reset scheme based on threshold subtraction results in a modulo-based reset scheme which allows to characterize LIF as a quantization operator based on a weighted Alexiewicz norm $\|.\|_{A, \alpha}$ with leaky parameter $\alpha$. We prove the quantization formula $\|\eta_f - f\|_{A, \alpha} < \vartheta$ under the general condition of local integrability, almost everywhere boundedness and locally finitely many superimposed weighted Dirac pulses which provides a much larger signal space and more flexible sparse signal representation than manageable by classical signal processing.

Universal Sleep Decoder: Aligning awake and sleep neural representation across subjects

q-bio.NC updates on arXiv.org Edit "Universal Sleep Decoder: Aligning awake and sleep neural representation across subjects"
Decoding memory content from brain activity during sleep has long been a goal in neuroscience. While spontaneous reactivation of memories during sleep in rodents is known to support memory consolidation and offline learning, capturing memory replay in humans is challenging due to the absence of well-annotated sleep datasets and the substantial differences in neural patterns between wakefulness and sleep. To address these challenges, we designed a novel cognitive neuroscience experiment and collected a comprehensive, well-annotated electroencephalography (EEG) dataset from 134 subjects during both wakefulness and sleep. Leveraging this benchmark dataset, we developed the Universal Sleep Decoder (USD) to align neural representations between wakefulness and sleep across subjects and a real-time staging model comparable to offline staging algorithms. Our model achieves up to 23.00% and 21.15% offline, as well as 22.6% and 20.4% real-time top-1 zero-shot real-time decoding accuracy on unseen subjects for N2/3 stage and REM stage, which is much higher than the decoding performances using individual sleep data. Furthermore, fine-tuning USD on test subjects enhances decoding accuracy to 29.20% and 30.47% offline, as well as 27.9% and 29.4% real-time top-1 accuracy, a substantial improvement over the baseline chance of 6.7%. Model comparison and ablation analyses reveal that our design choices, including the use of (i) an additional contrastive objective to integrate awake and sleep neural signals and (ii) a shared encoder to enhance the alignment of awake and sleep neural signals, significantly contribute to these performances. Collectively, our findings and methodologies represent a significant advancement in the field of sleep decoding.