A Novel P300 Classification Algorithm Based on a Principal Component Analysis-Convolutional Neural Network

Abstract

Aiming at enhancing the classification accuracy of P300 Electroencephalogram signals in a non-invasive brain–computer interface system, a novel P300 electroencephalogram signals classification algorithm is proposed which is based on improved convolutional neural network. In the data preprocessing part, the proposed P300 classification algorithm used the Principal Component Analysis algorithm to not only remove the noise and artifacts in the data, but also increase the data processing speed. Furthermore, the proposed P300 classification algorithm employed the parallel convolution method to improve the traditional convolutional neural network framework, which can increase the network depth and improve the network’s ability to classify P300 electroencephalogram signals. The proposed algorithm was evaluated by two datasets (the dataset from the competition and the dataset from the laboratory). The results show that, in the dataset I, the proposed P300 classification algorithm could obtain accuracy rates higher than 95%, and achieve one of the best performances in four classification algorithms, while, in the dataset II, the proposed P300 classification algorithm can get accuracy rates higher than 90%, and is superior to the other three algorithms in all ten subjects. These demonstrated the effectiveness of the proposed algorithm. The proposed classification algorithm can be applied in the actual brain–computer interface systems to help people with disability in the daily lives.

Real-Time Implementation of EEG Oscillatory Phase-Informed Visual Stimulation Using a Least Mean Square-Based AR Model

“…One advantage of our proposed method is the low computational cost. Some prior studies focused on using machine learning methods for phase estimation, and a variety of machine learning techniques, particularly deep learning, have been applied in brain-computer interface (BCI) systems [ 18 , 35 , 36 ]. The main drawback of such procedures is the demand for preliminary data for training prior to the principal experiment.…”

A Single-Trial P300 Detector Based on Symbolized EEG and Autoencoded-(1D)CNN to Improve ITR Performance in BCIs

“…Additionally, no explicit information has been provided by the Wadsworth Research Center NYS Department of Health for data collection routines at the international BCI competition III. EEG channels' data have been provided with different SNRs, and a lot of outliers have been recorded on the Fz channels, which is one of the most informational ones according to the study conducted by authors in [22]. The data collection routine for the car prototype fluid driving has been-instead-conducted in a controlled environment, by regulating lights and minimizing distracting phenomena and with a real-time impedance check on each electrode.…”

“…For the sake of a coherent comparison, the same metrics were extracted from other state-of-the-art solutions which analyzed the same dataset: SVM-1 [13], CNN-1 [12], BN3 [10], ConvLSTM [11], their ensemble provided by [11], PCA-NN [22], the ERPENet [24]. Table 4 reports the comparison results over the BCI competition dataset (Dataset 2).…”

“…However, no explicit reference is reported with respect to the accuracy extraction modalities (e.g., validation or testing related accuracy). PCA-CNN [22] and SVM-1 [13] do not provide the investigated metrics. Moreover, Table 4 reports two versions of the here-proposed autoencoded CNN.…”

“…Similar results were achieved in [21], where a 3D recurrent neural network (3DRNN) was used to detect a P300 signal in a single trial. Finally, authors in [22] proposed the introduction of a preprocessing stage based on principal component analysis (PCA) to improve the CNN results. The proposed system demonstrated a slight improvement of the results, starting from eight repetitions up to 15.…”