The hybrid brain-computer interface (BCI)'s multimodal technology enables precision brain-signal classification that can be used in the formulation of control commands. In present study, an experimental near-infrared spectroscopy-electroencephalography (NIRS-EEG) technique was to extract and decode four different types brain signals. NIRS setup positioned over prefrontal region, EEG left right motor cortex regions. Twelve subjects participating experiment were shown direction symbols,...

In this paper, a hybrid electroencephalography-functional near-infrared spectroscopy (EEG-fNIRS) scheme to decode eight active brain commands from the frontal region for brain-computer interface is presented. A total of are decoded by fNIRS, as positioned on prefrontal cortex, and EEG, around frontal, parietal, visual cortices. Mental arithmetic, mental counting, rotation, word formation tasks with in which selected features classification command generation peak, minimum, mean ΔHbO values...

We use functional near-infrared spectroscopy (fNIRS) to discriminate the alert and drowsy states for a passive brain-computer interface (BCI).The brain signals state are acquired from prefrontal dorsolateral cortex.The experiment is performed on 13 healthy subjects using driving simulator, their activity recorded continuous-wave fNIRS system.Linear discriminant analysis (LDA) employed training testing, data prefrontal, left-and right-dorsolateral regions.For classification, eight features...

In this paper, a deep-learning-based driver-drowsiness detection for brain-computer interface (BCI) using functional near-infrared spectroscopy (fNIRS) is investigated. The passive brain signals from drowsiness were acquired 13 healthy subjects while driving car simulator. activities measured with continuous-wave fNIRS system, in which the prefrontal and dorsolateral cortices focused. Deep neural networks (DNN) pursued to classify drowsy alert states. For training testing models,...

Cognitive workload is one of the widely invoked human factor in areas Human Machine Interaction (HMI) and Neuroergonomics. The precise assessment cognitive mental (MWL) vital requires accurate neuroimaging to monitor evaluate states brain. In this study, we have decoded four classes using long-short term memory (LSTM) with 89.31% average accuracy for brain-Computer Interface (BCI). brain activity signals are acquired functional Near-Infrared Spectroscopy (fNIRS) from prefrontal cortex (PFC)...

This research presents a brain-computer interface (BCI) framework for brain signal classification using deep learning (DL) and machine (ML) approaches on functional near-infrared spectroscopy (fNIRS) signals. fNIRS signals of motor execution walking rest tasks are acquired from the primary cortex in brain's left hemisphere nine subjects. DL algorithms, including convolutional neural networks (CNNs), long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM) used to achieve average...

Robot-mediated therapies for autism spectrum disorder (ASD) have shown promising results in the past. We proposed a novel mathematical model based on an adaptive multi-robot therapy of ASD children focusing two main impairments autism: 1) joint attention and 2) imitation. Joint intervention is three different least-to-most (LTM) cues, whereas imitation module uses activation robot. The system as therapist without any external stimuli (from environment) to improve skills child. Another aspect...

Enhanced classification accuracy and a sufficient number of commands are highly demanding in brain computer interfaces (BCIs). For successful BCI, early detection time is essential. In this paper, we propose novel classifier using modified vector phase diagram the power electroencephalography (EEG) signal for prediction hemodynamic responses. EEG functional near-infrared spectroscopy (fNIRS) signals motor task (thumb tapping) were obtained concurrently. Upon resting state threshold circle...

In this paper, a novel methodology for feature extraction to enhance classification accuracy of functional near-infrared spectroscopy (fNIRS)-based two-class and three-class brain-computer interface (BCI) is presented.Novel features are extracted using vector-based phase analysis method. Changes in oxygenated [Formula: see text] de-oxygenated text]) haemoglobin used calculate four features: change cerebral blood volume ([Formula: text]), oxygen exchange vector magnitude (|L|) angle (k). the...

The constantly evolving human-machine interaction and advancement in sociotechnical systems have made it essential to analyze vital human factors such as mental workload, vigilance, fatigue, stress by monitoring brain states for optimum performance safety. Similarly, signals become paramount rehabilitation assistive purposes fields brain-computer interface (BCI) closed-loop neuromodulation neurological disorders motor disabilities. complexity, non-stationary nature, low signal-to-noise ratio...

Anomalous chromosomes are the cause of genetic diseases such as cancer, Alzheimer's, Parkinson's, epilepsy, and autism. Karyotype analysis is standard procedure for diagnosing disorders. Identifying anomalies often costly, time-consuming, heavily reliant on expert interpretation, requires considerable manual effort. Efforts being made to automate karyogram analysis. However, unavailability large datasets, particularly those including samples with chromosomal abnormalities, presents a...

Electromyography (EMG) signals have gained significant attention due to their potential applications in prosthetics, rehabilitation, and human-computer interfaces. However, the dimensionality of EMG signal features poses challenges achieving accurate classification reducing computational complexity. To overcome such issues, this paper proposes a novel approach that integrates feature reduction techniques with an artificial neural network (ANN) classifier enhance accuracy high-dimensional...

Control of active prosthetic hands using surface electromyography (sEMG) signals is an research area; despite the advances in sEMG pattern recognition and classification techniques, none commercially available provide user with intuitive control. One major reasons for this disparity between academia industry variation a dynamic environment as opposed to controlled laboratory conditions. This investigated effects signal on performance hand motion classifier due arm position also explored...

Machine learning is used for extraction of valuable information from data thus helping in exploration hidden patterns, leading to models that can be prediction. In the domain autonomous vehicles machine techniques have been applied several areas, vehicle platooning being one them. Vehicle a vital feature automated highways which provides key benefits fuel economy, road safety and environmental protection coupled with safe transportation. However, high computational cost associated numerical...

Optical-neuro-imaging based functional Near-Infrared Spectroscopy (fNIRS) has been in use for several years the fields of brain research to measure response activity and apply it such as Neuro-rehabilitation, Brain-Computer Interface (BCI) Neuroergonomics. In this paper we have enhanced classification accuracy a Mental workload task using novel Fixed-Value Modified Beer-Lambert law (FV-MBLL) method. The hemodynamic changes corresponding mental are measured from Prefrontal Cortex (PFC) fNIRS....

Functional near-infrared spectroscopy (fNIRS) is one of the latest noninvasive brain function measuring technique that has been used for purpose brain-computer interfacing (BCI). In this paper, we compare and analyze effect six most commonly filtering techniques (i.e., Gaussian, Butterworth, Kalman, hemodynamic response filter (hrf), Wiener, finite impulse response) on classification accuracies fNIRS-BCI. To conclude with best optimal a specific cortical task owing to region, divided our...

In the current era of technological advancements and rising human-machine interaction, urged vital importance human factors ergonomics in an industrial collaborative environment. These ergonomic needs have made it essential to analyze cognitive processes like mental workload (MWL), stress vigilance ecological Conventionally Electroencephalography (EEG) was used for assessment brain electrical activity but recently functional Near-Infrared Spectroscopy (fNIRS) has immerged as a better...

In this paper, we have proposed a novel control strategy for quadcopter using brain signals. A brain-computer interface (BCI) technology is developed hybrid electroencephalography - near-infrared spectroscopy (EEG-NIRS) system and two commands are used to operate the quadcopter. An active signal upon user's own will generated motor imagery task reactive by visual flickering of light. The command triggering navigate in forward direction. Linear discriminant analysis classify activity offline...

Recent research has shown reliability in robotic therapies for improvement core impairments of autism. To improve the efficiency communication using robots, this study evaluates effectiveness three different stimuli a intervention children with autism spectrum disorder. Three reinforcement presented least-to-most (LTM) order introduced therapy NAO robot are: visual (color variation), auditory and motion cues. The was tested on 12 ASD children, 4 out fall under mild category whereas 8 minimal...

Reliability of high precision linear motion system is one the main concerns in industrial and military systems. The performance repeatability these systems are influenced by their respective drives load bearings. A fault members severely affects safe working overall system. This paper gives a reliable intelligent approach to detect classify faults for based on deep learning methods. Accuracy identification highly dependent improved features extraction. For this purpose, novel Residual Twin...