EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications.

Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research

SigSegment: A Signal-Based Segmentation Algorithm for Identifying Anomalous Driving Behaviours in Naturalistic Driving Videos

In recent years, distracted driving has garnered considerable attention as it continues to pose a significant threat to public safety on the roads. This has increased the need for innovative solutions that can identify and eliminate distracted driving behavior before it results in fatal accidents. In this paper, we propose a Signal-Based anomaly detection algorithm that segments videos into anomalies and non-anomalies using a deep CNN-LSTM classifier to precisely estimate the start and end times of an anomalous driving event. In the phase of anomaly detection and analysis, driver pose background estimation, mask extraction, and signal activity spikes are utilized. A Deep CNN-LSTM classifier was applied to candidate anomalies to detect and classify final anomalies. The proposed method achieved an overlap score of 0.5424 and ranked 9th on the public leader board in the AI City Challenge 2023, according to experimental validation results

SmartDriver: an assistant for reducing stress and improve the fuel consumption

JARCA 2015: Actas de las XVII Jornadas de ARCA: Sistemas Cualitativos y sus Aplicaciones en Diagnosis, Robótica, Inteligencia Ambiental y Ciudades Inteligentes = Proceedings of the XVII ARCA Days: Qualitative Systems and its Applications in Diagnose Robotics, Ambient Intelligence and Smart Cities, Vinaros (Valencia), 23 al 27 de Junio de 2015The stress, safety and fuel consumption are variables that are strongly related. If the stress is high, the driver is more likely to make mistakes and have ac- cidents. In addition, he or she will make decisions at short notice. The acceleration and deceleration increases, minimizing the use of energy generated by the engine. However, the stress can be reduced if we provide information about the environment in ad- vance. In this paper, we propose a driving assistant which issues tips to the driver in order to improve the stress level. These tips are based on speed. The solution estimates the optimal average speed for each road section. In addition, the solution provides a slowdown profile when the user is close to a stress area. The objective is the initial vehicle speed minimizes the stress level and the sharp acceleration (positive and negative). In addition, the system em- ploys gamification tools to encourage the driver to follow the recommendations. On the other hand, the proposal provides information about the driver and the road state in an anonymous way in order to improve the management of the city traffic. The proposal is run on an Android device and the driver stress is estimated using non intrusives sensors and telemetry from the vehicle.The research leading to these results has received funding from the “HERMES-SMART DRIVER” project TIN2013-46801-C4-2-R within the Spanish "Plan Nacional de I+D+I" under the Spanish Ministerio de Economía y Competitividad and from the Spanish Ministerio de Economía y Competitividad funded projects (co-financed by the Fondo Europeo de Desarrollo Regional (FEDER)) IRENE (PT-2012-1036-and 370000), COMINN (IPT-2012-0883-430000) the within (IPT-2012-0882-430000) REMEDISS INNPACTO progra

A Data-Driven Framework to Model Physical Fatigue in Industrial Environments Using Wearable Technologies

Industry 4.0 is the tendency towards automation and data exchange in manufacturing and the process sector. However, many manual material handling and repetitive operations can still cause the operators fatigue or exhaustion. Once the operator experiences physical fatigue, their performance decreases. The consequences may result in reduced production quality and efficiency and increased operational human errors that could give rise to incidents and accidents. Over time, physical fatigue can result in more adverse effects for the operators, such as Chronic Fatigue Syndrome (CFS) and Work-related Musculoskeletal Disorders (WSMD). For this reason, from an occupational health and safety point of view, the operator’s hysical fatigue must be managed. The increasing availability of wearable devices combined with health information can provide real-time measuring and monitoring of physical fatigue in the operational environment while minimally influencing the primary job. This paper presents a physiological signal-based approach using a non-intrusive wristband for continuous health monitoring to predict physical fatigue in industrial-related tasks. These data are used as input to classification algorithms to detect physical fatigue. Accurate and real-time physical fatigue detection helps to improve operator safety and prevent work accidents. Future work will deploy the model in a real-world environment in the industry

Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Face and Eye Tracking

It is critical today to provide safe and collision-free transport. As a result, identifying the driver’s drowsiness before their capacity to drive is jeopardized. An automated hybrid drowsiness classification method that incorporates the artificial neural network (ANN) and the gray wolf optimizer (GWO) is presented to discriminate human drowsiness and fatigue for this aim. The proposed method is evaluated in alert and sleep-deprived settings on the driver drowsiness detection of video dataset from the National Tsing Hua University Computer Vision Lab. The video was subjected to various video and image processing techniques to detect the drivers’ eye condition. Four features of the eye were extracted to determine the condition of drowsiness, the percentage of eyelid closure (PERCLOS), blink frequency, maximum closure duration of the eyes, and eye aspect ratio (ARE). These parameters were then integrated into an ANN and combined with the proposed method (gray wolf optimizer with ANN [GWOANN]) for drowsiness classification. The accuracy of these models was calculated, and the results demonstrate that the proposed method is the best. An Adadelta optimizer with 3 and 4 hidden layer networks of (13, 9, 7, and 5) and (200, 150, 100, 50, and 25) neurons was utilized. The GWOANN technique had 91.18% and 97.06% accuracy, whereas the ANN model had 82.35% and 86.76%

A dynamic neural field model of temporal order judgments

Temporal ordering of events is biased, or influenced, by perceptual organization—figure–ground organization—and by spatial attention. For example, within a region assigned figural status or at an attended location, onset events are processed earlier (Lester, Hecht, & Vecera, 2009; Shore, Spence, & Klein, 2001), and offset events are processed for longer durations (Hecht & Vecera, 2011; Rolke, Ulrich, & Bausenhart, 2006). Here, we present an extension of a dynamic field model of change detection (Johnson, Spencer, Luck, & Schöner, 2009; Johnson, Spencer, & Schöner, 2009) that accounts for both the onset and offset performance for figural and attended regions. The model posits that neural populations processing the figure are more active, resulting in a peak of activation that quickly builds toward a detection threshold when the onset of a target is presented. This same enhanced activation for some neural populations is maintained when a present target is removed, creating delays in the perception of the target’s offset. We discuss the broader implications of this model, including insights regarding how neural activation can be generated in response to the disappearance of information. (PsycINFO Database Record (c) 2015 APA, all rights reserved

Review of medical data analysis based on spiking neural networks

Medical data mainly includes various types of biomedical signals and medical images, which can be used by professional doctors to make judgments on patients' health conditions. However, the interpretation of medical data requires a lot of human cost and there may be misjudgments, so many scholars use neural networks and deep learning to classify and study medical data, which can improve the efficiency and accuracy of doctors and detect diseases early for early diagnosis, etc. Therefore, it has a wide range of application prospects. However, traditional neural networks have disadvantages such as high energy consumption and high latency (slow computation speed). This paper presents recent research on signal classification and disease diagnosis based on a third-generation neural network, the spiking neuron network, using medical data including EEG signals, ECG signals, EMG signals and MRI images. The advantages and disadvantages of pulsed neural networks compared with traditional networks are summarized and its development orientation in the future is prospected

Neural correlates of visual awareness

openL'elaborato si propone di esporre le attuali evidenze riguardanti il modo in cui i contenuti soggettivi di consapevolezza visiva sono codificati a livello neurale. Sebbene i meccanismi neurali della percezione visiva siano ampiamente conosciuti, rimane ancora da chiarire come l'informazione visiva entri a far parte dei contenuti della coscienza. Per identificare i correlati neurali della coscienza (CNC), che rappresentano la minima attività neurale per una specifica esperienza conscia, vengono messe in relazione misure comportamentali di consapevolezza, limitatamente a stimoli presentati in un contesto sperimentale, con i sottostanti meccanismi neurali. Attraverso paradigmi sperimentali come la rivalità binoculare e tecniche di mascheramento visivo è possibile provare ad identificare i CNC contenuto-specifici utilizzando misure neurofisiologiche e tecniche di neuroimaging. Tali tecniche forniscono infatti utili informazioni circa le basi neuroanatomiche e funzionali dell'esperienza sotto esame. Sebbene i meccanismi che sottendono l’attenzione siano spesso associati all'esperienza cosciente, evidenze sperimentali suggeriscono una separazione tra i due processi. Le ricerche sui correlati neurali della consapevolezza visiva indicano come l’attività di una singola area cerebrale non possa essere necessaria e sufficiente a spiegare le qualità dei contenuti coscienti. Sembrerebbe invece essere necessaria una rappresentazione della scena visiva distribuita nella corteccia visiva primaria (V1) e nelle aree visive ventrali con attivazione di regioni temporo-parietali. Misure elettrofisiologiche come la visual awareness negativity (VAN) sono state correlate alla consapevolezza visiva mentre altri indicatori sembrerebbero essere maggiormente legati a processi attentivi. Diversi modelli teorici offrono spiegazioni empiriche sull’emergenza della coscienza dall’attività cerebrale. Nel caso della consapevolezza visiva, alcuni modelli teorici rilevanti sono la teoria dello spazio di lavoro neurale globale, la quale sottolinea la necessità di condivisione dell'informazione tra ampie aree cerebrali e la teoria dell'elaborazione ricorrente che si concentra invece sul feedback proveniente a V1 dalle aree extrastriate. Inoltre, il modello dell’”elaborazione predittiva” descrive la percezione cosciente come il risultato di un processo attivo in cui il cervello crea costantemente previsioni sull’ambiente circostante. Allo stato attuale, la ricerca sui correlati neurali della consapevolezza visiva evidenzia dunque come un network di regioni cerebrali posteriori sia fondamentale per avere esperienze visive coscienti. Inoltre, i segnali di feedback sembrano svolgere un ruolo cruciale, evidenziando le complesse interazioni tra dinamiche neurali e percezione cosciente.The paper aims to present the current evidence regarding how subjective contents of visual awareness are encoded at the neural level. While the neural mechanisms of visual perception are well understood, it remains unclear how visual information becomes part of consciousness. To identify the neural correlates of consciousness (NCC), representing the minimum neural activity for a specific conscious experience, behavioral measures of awareness are related to underlying neural mechanisms, limited to stimuli presented in an experimental context. Through experimental paradigms such as binocular rivalry and visual masking techniques, it is possible to attempt to identify content-specific NCC using neurophysiological measures and neuroimaging techniques. These techniques indeed provide valuable information about the neuroanatomical and functional basis of the examined experience. Although mechanisms underlying attention are often associated with conscious experience, experimental evidence suggests a separation between the two processes. Research on the neural correlates of visual awareness indicates that the activity of a single brain area may not be necessary and sufficient to explain the qualities of conscious contents. Instead, a distributed representation of the visual scene in the primary visual cortex (V1) and ventral visual areas with activation of temporo-parietal regions seems to be necessary. Electrophysiological measures such as Visual Awareness Negativity (VAN) have been correlated with visual awareness, while other indicators appear to be more related to attentional processes. Various theoretical models offer empirical explanations of the emergence of consciousness from brain activity. In the case of visual awareness, some relevant theoretical models include the global neural workspace theory, which emphasizes the need for information sharing among extensive brain areas, and the recurrent processing theory, which focuses on feedback from extrastriate areas to V1. Additionally, the predictive processing model describes conscious perception as the result of an active process in which the brain constantly generates predictions about the surrounding environment. Currently, research on the neural correlates of visual awareness highlights the importance of a network of posterior brain regions for conscious visual experiences. Furthermore, feedback signals appear to play a crucial role, highlighting the complex interactions between neural dynamics and conscious perception

Early-Stage Vision and Perceptual Imagery in Autism Spectrum Conditions

Autism spectrum conditions (ASC) are characterized by multifaceted alterations in visual perception and mental imagery. However, the interaction between early-stage visual perception and imagery has not been explored. We recruited 40 individuals with ASC and 20 neurotypical control volunteers to participate in a lateral masking task. Participants detected a luminance-contrast target pattern (Gabor patch) flanked by two collinear masks. The flanking masks inhibit target detection at small target-mask distances and facilitate target detection at intermediate target-mask distances. In the perceptual task, the masks appeared adjacent to the target. In the imagery task, participants imagined the masks immediately after seeing them. Results revealed that individuals with ASC characterized by exceptional visuoconstructional abilities (enhanced Block Design performance; n = 20) showed weaker inhibition at small target-mask distances and stronger facilitation at intermediate target-mask distances relative to the controls. Visual imagery was markedly dampened in ASC regardless of the visuoconstructional abilities. At the behavioral level, these results indicate increased facilitation via lateral connections in the primary visual cortex (V1) of individuals with ASC who exhibit exceptional visuoconstructional abilities, together with less efficient mental imagery