A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals

Abstract

A brain-computer interface (BCI) based on electroencephalography (EEG) can provide independent information exchange and control channels for the brain and the outside world. However, EEG signals come from multiple electrodes, the data of which can generate multiple features. How to select electrodes and features to improve classification performance has become an urgent problem to be solved. This paper proposes a deep convolutional neural network (CNN) structure with separated temporal and spatial filters, which selects the raw EEG signals of the electrode pairs over the motor cortex region as hybrid samples without any preprocessing or artificial feature extraction operations. In the proposed structure, a 5-layer CNN has been applied to learn EEG features, a 4-layer max pooling has been used to reduce dimensionality, and a fully-connected (FC) layer has been utilized for classification. Dropout and batch normalization are also employed to reduce the risk of overfitting. In the experiment, the 4 s EEG data of 10, 20, 60, and 100 subjects from the Physionet database are used as the data source, and the motor imaginations (MI) tasks are divided into four types: left fist, right fist, both fists, and both feet. The results indicate that the global averaged accuracy on group-level classification can reach 97.28%, the area under the receiver operating characteristic (ROC) curve stands out at 0.997, and the electrode pair with the highest accuracy on 10 subjects dataset is FC3-FC4, with 98.61%. The research results also show that this CNN classification method with minimal (2) electrode can obtain high accuracy, which is an advantage over other methods on the same database. This proposed approach provides a new idea for simplifying the design of BCI systems, and accelerates the process of clinical application.

Comparing between Different Sets of Preprocessing, Classifiers, and Channels Selection Techniques to Optimise Motor Imagery Pattern Classification System from EEG Pattern Recognition

“…To the best of our knowledge, we provide better overall classification results by comparing the results obtained with other studies working with the same data sets [ 58 , 59 , 60 , 61 ]. In particular, in Roots et al [ 58 ], the 83.8% accuracy was obtained between the imagery movement task with the EEGNet Fusion, while in the proposed approach using ERD_AB and the optimal channels combinations with SVM classifier we reached 91% of the accuracy in the same task.…”

Deep Convolutional Neural Network Regularization for Alcoholism Detection Using EEG Signals

“…CNNs have been used in problems such as speech recognition, image classification, recommender systems, and text classification. More recently, CNNs have been shown to classify EEG brain signals for autism [ 46 ], epilepsy [ 46 , 47 , 48 , 49 ], seizure detection in children [ 50 ], schizophrenia [ 51 ], brain–computer interface (BCI) [ 52 ], alcoholism predisposition [ 21 , 37 ], drowsiness detection [ 36 , 53 ], and neurodegeneration and physiological aging [ 54 ] into normal and pathological groups of young and old people.…”

Deteksi Myocardial Infarction Menggunakan Fitur Statistik Segmen-ST Elektrokardiogram dan Analisis Diskriminan

“…Dari eksperimen ini didapatkan hasil akurasi tertinggi tanpa mengghilangkan derau [12]. Metode CNN juga telah digunakan pada beberapa penelitian dan menunjukkan akurasi yang tinggi menggunakan metode lainnya dengan basis data yang sama dengan eksperimen yang menggabungkan empat lapisan [13].…”