A new parameter tuning approach for enhanced motor imagery EEG signal classification

Abstract

A brain-computer interface (BCI) system allows direct communication between the brain and the external world. Common spatial pattern (CSP) has been used effectively for feature extraction of data used in BCI systems. However, many studies show that the performance of a BCI system using CSP largely depends on the filter parameters. The filter parameters that yield most discriminating information vary from subject to subject and manually tuning of the filter parameters is a difficult and time-consuming exercise. In this paper, we propose a new automated filter tuning approach for motor imagery electroencephalography (EEG) signal classification, which automatically and flexibly finds the filter parameters for optimal performance. We have evaluated the performance of our proposed method on two public benchmark datasets. Compared to the existing conventional CSP approach, our method reduces the average classification error rate by 2.89% and 3.61% for BCI Competition III dataset IVa and BCI Competition IV dataset I, respectively. Moreover, our proposed approach also achieved lowest average classification error rate compared to state-of-the-art methods studied in this paper. Thus, our proposed method can be potentially used for developing improved BCI systems, which can assist people with disabilities to recover their environmental control. It can also be used for enhanced disease recognition such as epileptic seizure detection using EEG signals. Graphical abstract ᅟ.

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

“…The next preprocessing stage, band-pass filtering, extracts the frequencies of interest, accomplished by a fifth order Butterworth filter, the most widely used filter for MI-EEG signals (Yang et al, 2017;Kumar and Sharma, 2018). For MI brain activity, physiologists and neuroscientists normally focus on five frequency bands: alpha (8-13 Hz), sigma (13-18 Hz), low beta (18-23 Hz), high beta (23-28 Hz), and low gamma (28)(29)(30)(31)(32)(33)(34)(35).…”

PRICAI 2019: Trends in Artificial Intelligence

“…Filtering the signal using appropriate temporal filter to obtain as much important information as possible is a vital step in a BCI system. Here, we employ the method proposed in our previous work [49]. The three main parameters of a Butterworth bandpass filter (filter order, lower cutoff frequency and upper cutoff frequency) are optimized.…”

“…Genetic algorithm (GA) was employed for this purpose. In this work, we extend our previous work [49] by proposing the use of common spatial spectral pattern (CSSP) instead of CSP to further improve the scheme. This is a simple yet an effective approach (mostly ignored by researchers in this field) that improves the spatial resolution of the signal resulting in improved performance.…”

“…However, it requires high computational power in the training phase as the number of channels is increased. To tackle this problem, we proposed a scheme to find a single frequency band that will contain the most discriminative information between the MI tasks [49]. Genetic algorithm (GA) was employed for this purpose.…”

Feature recognition of motor imaging EEG signals based on deep learning

“…Based on the original research, Subasi changed the classification method and introduced SVM. The feature extraction introduced ICA, PCA, LDA, and other methods [9]. The accuracy rate of this time has increased to over 98%.…”