Auto-correlation Based Feature Extraction Approach for EEG Alcoholism Identification

Sadiq, Muhammad Tariq; Siuly, Siuly; Rehman, Ateeq Ur; Wang, Hua

doi:10.1007/978-3-030-90885-0_5

Cited by 9 publications

(3 citation statements)

References 36 publications

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“…While showing and internally calculating three-dimensional graphics, all vertices are expected to be represented by homogeneous four-dimensional coordinates [35][36][37][38][39][40]. The model uses the 3D coordinates (x, y, and z) to the homogeneous D coordinates (X, Y, Z, and P).…”

Section: Structural Roaming Sorting Algorithmmentioning

confidence: 99%

Application of Wireless Sensor Network Integrated With 3-5g Technology in the Design Of Interactive Space in an Urban Landscape

Zhang

2023

EAI Endorsed Scal Inf Syst

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INTRODUCTION: The conventional approaches of urban landscape design and planning cannot incorporate concepts into the complete planning processes for scenes, and they need to effectively, clearly, and coordinately convey the planning impacts of several systems and huge scenes. Virtual Reality (VR) is now a significant part of designing and planning urban landscapes because of the advancement of Wireless Sensor Networks (WSNs) and 5G technologies. OBJECTIVES: This article discusses the current condition and importance of "Three-Dimensional (3D)" portrayed urban landscape design and planning by outlining and evaluating previous works, introducing roaming algorithms and establishing a 3D database, and developing virtual scene manipulation tools. METHODS: In this approach, the 3D model CapsNet (Capsule Network) neural model is implemented to train the data and provide improved results by highlighting the interactive space in the urban landscaper. CapsNet can improve the computer vision tasks of VR and AR applications. RESULTS: The results show that a 3D visualized system with the CapsNet model can quickly distribute a large amount of data through graphics, enabling experts to intuitively notice testing results or interactively process such findings. Additionally, VR provides designers with practical data processing impacts through smart drawing, comprehensive display, and timely reporting, significantly increasing design productivity and effectively facilitating design quality.

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Section: Structural Roaming Sorting Algorithmmentioning

confidence: 99%

Application of Wireless Sensor Network Integrated With 3-5g Technology in the Design Of Interactive Space in an Urban Landscape

Zhang

2023

EAI Endorsed Scal Inf Syst

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“…The KNN is a simple classifi er that classifi es each of input data based on the nearest train data points (51,52), for example when assuming that two features have been extracted and used as train data in a KNN classifi er (Fig. 6).…”

Section: Classifi Ersmentioning

confidence: 99%

“…In most patients with epilepsy, there are no defi nite clinical signs for seizures. Electroencephalography (EEG) signals are highly utilized in detecting brain activities and disorders such as depression (3,4), braincomputer interface (BCI) (5)(6)(7), schizophrenia (8), alcoholism (9) and sleep apnea (10). Spikes are regularly evident in EEG signals of the human brain for epileptic seizures (11,12).…”

Section: Introductionmentioning

confidence: 99%

Recognizing seizure using Poincaré plot of EEG signals and graphical features in DWT domain

Akbari¹,

Sadiq²,

Jafari³

et al. 2022

BLL

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Electroencephalography (EEG) signals are considered one of the oldest techniques for detecting disorders in medical signal processing. However, brain complexity and the non-stationary nature of EEG signals represent a challenge when applying this technique. The current paper proposes new geometrical features for classifi cation of seizure (S) and seizure-free (SF) EEG signals with respect to the Poincaré pattern of discrete wavelet transform (DWT) coeffi cients. DWT decomposes EEG signal to four levels, and thus Poincaré plot is shown for coeffi cients. Due to patterns of the Poincaré plot, novel geometrical features are computed from EEG signals. The computed features are involved in standard descriptors of 2-D projection (STD), summation of triangle area using consecutive points (STA), as well as summation of shortest distance from each point relative to the 45-degree line (SSHD), and summation of distance from each point relative to the coordinate center (SDTC). The proposed procedure leads to discriminate features between S and SF EEG signals. Thereafter, a binary particle swarm optimization (BPSO) is developed as an appropriate technique for feature selection. Finally, k-nearest neighbor (KNN) and support vector machine (SVM) classifi ers are used for classifying features in S and SF groups. By developing the proposed method, we have archived classifi cation accuracy of 99.3 % with respect to the proposed geometrical features. Accordingly, S and SF EEG signals have been classifi ed. Also, Poincaré plot of SF EEG signals has more regular geometrical shapes as compared to S group. As a fi nal remark, we notice that the Poincaré plot of coeffi cients in S EEG signals has occupied more space as compared to SF EEG signals (Tab. 3, Fig. 11, Ref. 57).

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Fractional Fourier Transform Aided Computerized Framework for Alcoholism Identification in EEG

Sadiq

Akbari

Siuly

et al. 2022

Lecture Notes in Computer Science

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Auto-correlation Based Feature Extraction Approach for EEG Alcoholism Identification

Cited by 9 publications

References 36 publications

Application of Wireless Sensor Network Integrated With 3-5g Technology in the Design Of Interactive Space in an Urban Landscape

Application of Wireless Sensor Network Integrated With 3-5g Technology in the Design Of Interactive Space in an Urban Landscape

Recognizing seizure using Poincaré plot of EEG signals and graphical features in DWT domain

Fractional Fourier Transform Aided Computerized Framework for Alcoholism Identification in EEG

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