Identification of vowels in consonant–vowel–consonant words from speech imagery based EEG signals
نویسندگان
چکیده
منابع مشابه
Classification of EEG Signals for Discrimination of Two Imagined Words
In this study, a Brain-Computer Interface (BCI) in Silent-Talk application was implemented. The goal was an electroencephalograph (EEG) classifier for three different classes including two imagined words (Man and Red) and the silence. During the experiment, subjects were requested to silently repeat one of the two words or do nothing in a pre-selected random order. EEG signals were recorded by ...
متن کاملIdentification of Anisomerous Motor Imagery EEG Signals Based on Complex Algorithms
Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI pattern recognition system that is based on complex algorithms for classifying MI EEG signals. In ...
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 کاملICA-Based Imagined Conceptual Words Classification on EEG Signals
Independent component analysis (ICA) has been used for detecting and removing the eye artifacts conventionally. However, in this research, it was used not only for detecting the eye artifacts, but also for detecting the brain-produced signals of two conceptual danger and information category words. In this cross-sectional research, electroencephalography (EEG) signals were recorded using Microm...
متن کاملIdentification of Motor Imagery Movements from EEG Signals Using Automatically Selected Features in the Dual Tree Complex Wavelet Transform Domain
The decoding of human brain electrical functions by electroencephalogram (EEG) signal is the most important step in brain computer interface (BCI) based systems. So, in this paper, an automatic feature selection method has been proposed to classify imagery left and right hand movements from the EEG signals in the Dual Tree Complex Wavelet Transform domain. First, the EEG signals are decomposed ...
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: Cognitive Neurodynamics
سال: 2019
ISSN: 1871-4080,1871-4099
DOI: 10.1007/s11571-019-09558-5