Real Time Sleep Onset Detection from Single Channel EEG Signal Using Block Sample Entropy

Zobaed, Talha; Ahmed, Saadaldeen Rashid; Miah, Abu Saleh Musa; Binta, Salma Masuda; Ahmed, Mohammed Rashid; Rashid, Mamunur

doi:10.1088/1757-899x/928/3/032021

Cited by 15 publications

(4 citation statements)

References 12 publications

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“…However, it takes a lot of time and effort to set up a BCI system with the sensors. Additionally, it is a significant obstacle to integrating BCI into commonplace applications [13,15,16]. A survey includes new frontiers in applying deep learning for brain signal analysis [17].…”

Section: Related Workmentioning

confidence: 99%

Motor Imagery Classification Using Effective Channel Selection of Multichannel EEG

Shiam,

Hassan,

Islam

et al. 2024

Brain Sciences

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Electroencephalography (EEG) is effectively employed to describe cognitive patterns corresponding to different tasks of motor functions for brain–computer interface (BCI) implementation. Explicit information processing is necessary to reduce the computational complexity of practical BCI systems. This paper presents an entropy-based approach to select effective EEG channels for motor imagery (MI) classification in brain–computer interface (BCI) systems. The method identifies channels with higher entropy scores, which is an indication of greater information content. It discards redundant or noisy channels leading to reduced computational complexity and improved classification accuracy. High entropy means a more disordered pattern, whereas low entropy means a less disordered pattern with less information. The entropy of each channel for individual trials is calculated. The weight of each channel is represented by the mean entropy of the channel over all the trials. A set of channels with higher mean entropy are selected as effective channels for MI classification. A limited number of sub-band signals are created by decomposing the selected channels. To extract the spatial features, the common spatial pattern (CSP) is applied to each sub-band space of EEG signals. The CSP-based features are used to classify the right-hand and right-foot MI tasks using a support vector machine (SVM). The effectiveness of the proposed approach is validated using two publicly available EEG datasets, known as BCI competition III–IV(A) and BCI competition IV–I. The experimental results demonstrate that the proposed approach surpasses cutting-edge techniques.

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Section: Related Workmentioning

confidence: 99%

Motor Imagery Classification Using Effective Channel Selection of Multichannel EEG

Shiam,

Hassan,

Islam

et al. 2024

Brain Sciences

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“…Static sign language recognition, continuous sign language recognition and dynamic sign language recognition are different categories of sign language. For decades, several kinds of machine learning and deep learning algorithms have been proposed for EMG, ECG, Image-based emotion, activity, and sign language recognition systems [5], [7], [8], [10], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28]. Every country has a different sign language, so developing SLR systems for the specific sign language has been attempted worldwide.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Korean Sign Language Recognition Using Pose Estimation Based and Attention-Based Neural Network

Shin,

Miah,

Suzuki

et al. 2023

IEEE Access

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Sign language recognition is crucial for improving communication accessibility for the hearing impaired community and reducing dependence on human interpreters. Notably, while significant research efforts have been devoted to many prevalent languages, Korean Sign Language (KSL) remains relatively underexplored, particularly concerning dynamic signs and generalizability. The scarcity of KSL datasets has exacerbated this limitation, hindering progress. Furthermore, most KSL research predominantly relies on static image-based datasets for recognition, leading to diminished accuracy and the inability to detect dynamic sign words. Furthermore, most KSL research predominantly relies on static image-based datasets for recognition, leading to diminished accuracy and the inability to detect dynamic sign words. Additionally, existing KSL recognition systems grapple with suboptimal performance accuracy and heightened computational complexity, further emphasizing the existing research gap. To address these formidable challenges, we propose a robust dynamic KSL recognition system that combines a skeleton-based Graph Convolution network with an attention-based neural network, effectively bridging the gap. Our solution employs a two-stream deep learning network to navigate the intricacies of dynamic signs, enhancing accuracy by effectively handling non-connected joint skeleton features. In this system, the first stream meticulously processes 47 pose landmarks using the Graph Convolutional Network (GCN) to extract graph-based features. These features are meticulously refined through a channel attention module and a general CNN, enhancing their temporal context. Concurrently, the second stream focuses on joint motionbased features, employing a similar approach. Subsequently, these distinct features from both streams are harmoniously integrated and channelled through a classification module to achieve precise sign-word recognition. A significant contribution of our work lies in creating a novel KSL video dataset, addressing the scarcity of data in this domain. This dataset comprises comprehensive information, including skeletal data from 47 joint skeleton points and details from both hands, body, and facial expressions. Our dataset aims to fill a critical gap in KSL research and provides a solid foundation for more extensive and inclusive studies in the field. Through this innovative approach, we aim to contribute significantly to the field of KSL recognition, filling the gaps in dynamic sign recognition and bolstering the accessibility of sign language communication within the Korean hearing impaired community and beyond. Our evaluations on a benchmark KSL-77 dataset and our proprietary lab dataset resulted in recognition accuracies of 99.87% and 100%, respectively. These results highlight the superiority of our model in the KSL recognition domain, outperforming existing models in terms of accuracy and computational efficiency.The associate editor coordinating the review of this manuscript and approving it for publication was Yon...

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“…All the algorithms used in this domain can be divided into the following categories: (I) sensor-based systems for monitoring the person [41,42]; (II) radio frequency (RF) sensor-based systems, and (III) camera-based vision-related systems. Many researchers record various signals with various sensors such as gyroscopes, accelerometers, EMGs, and EEGs to collect information from many people, not just the elderly [43][44][45][46][47][48][49][50][51][52]. Then they extract various kinds of features, including angle, distance, the sum of X and Y with various directions and their derivatives, and geometrical, statistical, and mathematical formulas [39].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Fall Detection Using Graph-Based Spatial Temporal Convolution and Attention Network

et al. 2023

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The prevention of falls has become crucial in the modern healthcare domain and in society for improving ageing and supporting the daily activities of older people. Falling is mainly related to age and health problems such as muscle, cardiovascular, and locomotive syndrome weakness, etc. Among elderly people, the number of falls is increasing every year, and they can become life-threatening if detected too late. Most of the time, ageing people consume prescription medication after a fall and, in the Japanese community, the prevention of suicide attempts due to taking an overdose is urgent. Many researchers have been working to develop fall detection systems to observe and notify about falls in real-time using handcrafted features and machine learning approaches. Existing methods may face difficulties in achieving a satisfactory performance, such as limited robustness and generality, high computational complexity, light illuminations, data orientation, and camera view issues. We proposed a graph-based spatial-temporal convolutional and attention neural network (GSTCAN) with an attention model to overcome the current challenges and develop an advanced medical technology system. The spatial-temporal convolutional system has recently proven the power of its efficiency and effectiveness in various fields such as human activity recognition and text recognition tasks. In the procedure, we first calculated the motion along the consecutive frame, then constructed a graph and applied a graph-based spatial and temporal convolutional neural network to extract spatial and temporal contextual relationships among the joints. Then, an attention module selected channel-wise effective features. In the same procedure, we repeat it six times as a GSTCAN and then fed the spatial-temporal features to the network. Finally, we applied a softmax function as a classifier and achieved high accuracies of 99.93%, 99.74%, and 99.12% for ImViA, UR-Fall, and FDD datasets, respectively. The high-performance accuracy with three datasets proved the proposed system’s superiority, efficiency, and generality.

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Real Time Sleep Onset Detection from Single Channel EEG Signal Using Block Sample Entropy

Cited by 15 publications

References 12 publications

Motor Imagery Classification Using Effective Channel Selection of Multichannel EEG

Motor Imagery Classification Using Effective Channel Selection of Multichannel EEG

Dynamic Korean Sign Language Recognition Using Pose Estimation Based and Attention-Based Neural Network

Dynamic Fall Detection Using Graph-Based Spatial Temporal Convolution and Attention Network

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