A SVM based classification of EEG for predicting the movement intent of human body

Li, Kaiyang; Zhang, Xiaodong; Du, Yuhuan

doi:10.1109/urai.2013.6677297

Cited by 9 publications

(7 citation statements)

References 7 publications

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“…DVM son yıllarda örüntü sınıflandırma ve regresyon için son derece güçlü ve önemli olarak ortaya çıkmıştır. DVM, EEG sinyallerinin sınıflandırmasında yoğun olarak kullanılmaktadır, depresyon hastaları tanınması [31], insan vücudunun hareket amacını tahmin etmesi [32], epileptik nöbetin sınıflandırılması [33] gibi ve daha pek çok çalışmalar vardır.…”

Section: Destek Vektör Makineleriunclassified

Feature extraction with wavelet transform for EEG Signal based anxiety classification

OMAR

Tepe

2023

NÖHÜ Müh. Bilim. Derg.

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Anksiyete, üretkenliği ve yaşam kalitesini etkilediği kadar insan yeteneklerini ve davranışlarını da etkiler. Depresyon ve intiharın ana nedeni olarak kabul edilebilir. Günümüzde klinisyenler anksiyete bozukluklarını teşhis etmek için belirli kriterler kullanılmaktadır. Anksiyete tespitinin karmaşık görevini yerine getiren, invaziv olmayan güvenilir tekniklere ihtiyaç vardır. Bu çalışma, elektroensefalografi (EEG) sinyallerini analiz ederek ikili ve dörtlü sınıfları daha az EEG kanalı ve öznitelik sınıflandırmayı amaçlamıştır. 23 kişinin 14 kanallı EEG sinyalini içeren DASPS veri tabanı kullanılmıştır. EEGLAB kullanarak 14 kanaldan 4 kanal seçilmiştir. Öznitelik çıkarımı için dalgacık dönüşümü kullanılmıştır. MATLAB Classification learner toolbox’taki 8 yöntem ile sınıflandırma yapılmıştır. En yüksek doğrulukta başarımlar ikili sınıflandırmada %67,1 doğrulukta Karar ağaçları yönteminde, dörtlü sınıflandırmada %58,5 doğrulukta destek vektör makinesi ile elde edilmiştir.

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Section: Destek Vektör Makineleriunclassified

Feature extraction with wavelet transform for EEG Signal based anxiety classification

OMAR

Tepe

2023

NÖHÜ Müh. Bilim. Derg.

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“…It consists of a set of conditions or rules organized in a hierarchical structure where the final decision can be determined following conditions established from the root to its leaves. SVM [ 35 , 36 , 37 ] refers to supervised learning models used to classify data into two categories by finding an optimal hyperplane that separates two possible values for the variable

. If the data can be separated linearly, the hyperplanes divide SVM input data into two initial subgroups by assigning {−1, +1} tags.…”

Section: Preliminariesmentioning

confidence: 99%

Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals

Batres-Mendoza

Montoro-Sanjose

Guerra-Hernandez

et al. 2016

Sensors

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Quaternions can be used as an alternative to model the fundamental patterns of electroencephalographic (EEG) signals in the time domain. Thus, this article presents a new quaternion-based technique known as quaternion-based signal analysis (QSA) to represent EEG signals obtained using a brain-computer interface (BCI) device to detect and interpret cognitive activity. This quaternion-based signal analysis technique can extract features to represent brain activity related to motor imagery accurately in various mental states. Experimental tests in which users where shown visual graphical cues related to left and right movements were used to collect BCI-recorded signals. These signals were then classified using decision trees (DT), support vector machine (SVM) and k-nearest neighbor (KNN) techniques. The quantitative analysis of the classifiers demonstrates that this technique can be used as an alternative in the EEG-signal modeling phase to identify mental states.

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“…Time domain features have been widely used for their classification performance and low computational complexity. 44 Moreover, these features have been reported in other classification studies [41][42][43] as well.…”

Section: Signal Processing and Feature Extractionmentioning

confidence: 53%

“…• Electroencephalography Electroencephalography (EEG) is the method of acquiring brain activity, in form of electrical signals, by placing the electrodes on the skull. The methods has been investigated to detect upper and lower limb movement intentions [41][42][43]. Wang and Makeig [44] conducted a study on binary single-trial EEG classification i.e.…”

Section: Cognitive Interactionmentioning

confidence: 99%

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FMG based upper limb motion detection methods, performance analysis and control of assistive exoskeletons

Islam¹

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Exoskeletons are wearable devices designed to assist humans according to their needs. Their applications can be found in rehabilitation, assistance and power augmentation. For assistive powered exoskeletons human motion intention detection is an important element for implementing assistive control strategies. While many methods of motion detection have been developed, however, there still exists many challenges i.e. high robustness, convenience, data repeatability and applicability for implementation on assistive powered exoskeletons. Therefore, new methods that can fulfill these requirements are needed.The aim of this thesis is to develop novel methods of motion intention detection for control of exoskeletons. The focus of this thesis is to analyze the performance of force myography (FMG) to detect upper limb movements and based on it develop control methods for upper limb assistive exoskeletons.In this thesis performance of FMG is analyzed by comparing it with sEMG. Motion detection accuracy and data repeatability were compared for detecting forearm motions i.e. forearm flexion, extension, pronation, supination and rest. The study showed the feasibility of FMG when implemented for assistive powered exoskeleton control.Exoskeleton control with FMG is another focus of this thesis. FMG is first used to control a soft exoskeleton by detecting dynamic hand gestures i.e. rest, opening, closing and grasping. This study addressed the challenges associated with object grasping task i.e. amount of training data, robust detection and assistance level determination. The influence of sensor placement on detection performance was also experimentally analyzed.Finally, FMG based control method for upper limb exoskeleton, i.e. elbow and shoulder joint, is presented in this thesis. A machine learning based algorithm is developed for determining assistance level during object carrying tasks by estimating the carried payload. The performance of the method is analyzed by testing on healthy subjects. Whereas, the results of physical assistance are verified by comparing the results of load carrying tasks with and without exoskeleton.This thesis contributes to the state-of-the-art of upper limb motion iniii tention detection using FMG. Studies verify that FMG, being accurate and a convenient method to interpret motion intention, has great potential for application of assistive exoskeletons. A contribution of this thesis is performance analysis of muscle activity detection methods that compares FMG and sEMG in terms of accuracy/repeatability. Another contribution is the novel methods for grasping and load carrying. The proposed techniques are able to reduce system complexity for convenient and robust use in actual environment.

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A SVM based classification of EEG for predicting the movement intent of human body

Cited by 9 publications

References 7 publications

Feature extraction with wavelet transform for EEG Signal based anxiety classification

Feature extraction with wavelet transform for EEG Signal based anxiety classification

Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals

FMG based upper limb motion detection methods, performance analysis and control of assistive exoskeletons

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