Binarization Methods for Motor-Imagery Brain–Computer Interface Classification
نویسندگان
چکیده
منابع مشابه
A Study of Various Feature Extraction Methods on a Motor Imagery Based Brain Computer Interface System
Introduction: Brain Computer Interface (BCI) systems based on Movement Imagination (MI) are widely used in recent decades. Separate feature extraction methods are employed in the MI data sets and classified in Virtual Reality (VR) environments for real-time applications. Methods: This study applied wide variety of features on the recorded data using Linear Discriminant Analysis (LDA) classifie...
متن کاملClassification of EEG-based motor imagery BCI by using ECOC
AbstractAccuracy in identifying the subjects’ intentions for moving their different limbs from EEG signals is regarded as an important factor in the studies related to BCI. In fact, the complexity of motor-imagination and low amount of signal-to-noise ratio for EEG signal makes this identification as a difficult task. In order to overcome these complexities, many techniques such as variou...
متن کاملCommon Spatial Patterns Feature Extraction and Support Vector Machine Classification for Motor Imagery with the SecondBrain
Recently, a large set of electroencephalography (EEG) data is being generated by several high-quality labs worldwide and is free to be used by all researchers in the world. On the other hand, many neuroscience researchers need these data to study different neural disorders for better diagnosis and evaluating the treatment. However, some format adaptation and pre-processing are necessary before ...
متن کاملa study of various feature extraction methods on a motor imagery based brain computer interface system
introduction: brain computer interface (bci) systems based on movement imagination (mi) are widely used in recent decades. separate feature extraction methods are employed in the mi data sets and classified in virtual reality (vr) environments for real-time applications. methods: this study applied wide variety of features on the recorded data using linear discriminant analysis (lda) classifier...
متن کاملNeuro-Fuzzy based Motor Imagery Classification for a Four Class Brain Machine Interface
Brain Machine Interface (BMI) provides a digital link between the brain and a device such as a computer, robot or wheelchair. This paper presents a BMI design using Neuro-Fuzzy classifiers for controlling a wheelchair using EEG signals. EEG signals during motor imagery (MI) of left and right hand movements are recorded noninvasively at the sensorimotor cortex. Four mental task signals are analy...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Journal on Emerging and Selected Topics in Circuits and Systems
سال: 2020
ISSN: 2156-3357,2156-3365
DOI: 10.1109/jetcas.2020.3031698