Classifying Mental Activities from Eeg-p300 Signals Using Adaptive Neural Networks
نویسندگان
چکیده
In this paper, a new adaptive neural network classifier (ANNC) of EEGP300 signals from mental activities is proposed. To overcome an overtraining of the classifier caused by noisy and non-stationary data, the EEG signals are filtered and their autoregressive (AR) properties are extracted using an AR model before being passed to the ANNC. For evaluation purposes, the same data in Hoffmann et al. (2008) were used. With and without the AR property extraction, the proposed ANNC could achieve 100% accuracy for all the subjects. To verify the performance improvement of the proposed classification scheme, a comparison of the ANNC and the conventional back-propagation neural network classifier was performed as well.
منابع مشابه
Evaluation of the Hidden Markov Model for Detection of P300 in EEG Signals
Introduction: Evoked potentials arisen by stimulating the brain can be utilized as a communication tool between humans and machines. Most brain-computer interface (BCI) systems use the P300 component, which is an evoked potential. In this paper, we evaluate the use of the hidden Markov model (HMM) for detection of P300. Materials and Methods: The wavelet transforms, wavelet-enhanced indepen...
متن کاملA hybrid EEG-based emotion recognition approach using Wavelet Convolutional Neural Networks (WCNN) and support vector machine
Nowadays, deep learning and convolutional neural networks (CNNs) have become widespread tools in many biomedical engineering studies. CNN is an end-to-end tool which makes processing procedure integrated, but in some situations, this processing tool requires to be fused with machine learning methods to be more accurate. In this paper, a hybrid approach based on deep features extracted from Wave...
متن کاملClassiication of Electroencephalogram Using Artiicial Neural Networks
In this paper, we will consider the problem of classifying electroencephalogram (EEG) signals of normal subjects, and subjects suuering from psychiatric disorder, e.g., obsessive compulsive disorder, schizophrenia, using a class of artiicial neural networks, viz., multi-layer perceptron. It is shown that the multilayer perceptron is capable of classifying unseen test EEG signals to a high degre...
متن کاملDeep Recurrent Convolutional Neural Networks for Classifying P300 Bci Signals
We develop and test three deep-learning recurrent convolutional architectures for learning to recognize single trial EEG event related potentials for P300 brain-computer interfaces (BCI)s. One advantage of the neural network solution is that it provides a natural way to share a lower-level feature space between subjects while adapting the classifier that works on that feature space. We compare ...
متن کاملImplementing a Smart Method to Eliminate Artifacts of Vital Signals
Background: Electroencephalography (EEG) has vital and significant applications in different medical fields and is used for the primary evaluation of neurological disorders. Hence, having easy access to suitable and useful signal is very important. Artifacts are undesirable confusions which are generally originated from inevitable human activities such as heartbeat, blinking of eyes and facial ...
متن کامل