Table 3 - uploaded by Ian Daly
Content may be subject to copyright.
Source publication
One of the major aims of BCI research is devoted to achieving faster and more efficient control of external devices. The identification of individual tap events in a motor imagery BCI is therefore a desirable goal. EEG is recorded from subjects performing and imagining finger taps with their left and right hands. A Differential Evolution based feat...
Context in source publication
Context 1
... trials are split into left vs. right taps in both the imagined and executed conditions and DE is applied to identify appro- priate channel-frequency band combinations to differentiate the conditions. Table 3 lists the channel-frequency band combinations DE identifies for differentiating left vs. right taps in the EEG recorded during both the executed and imagined tap conditions. The accuracies achieved by the classifier on the test set using the selected channels and frequency bands are also listed. ...
Similar publications
When a brain-computer interface (BCI) is designed, high classification accuracy is difficult to obtain for motor imagery (MI) electroencephalogram (EEG) signals in view of their relatively low signal-to-noise ratio. In this paper, a fused multidimensional classification method based on extreme tree feature selection (FMCM-ETFS) is proposed for disc...
Feature selection is an important step in building classifiers for high-dimensional data problems, such as EEG classification for BCI applications. This paper proposes a new wrapper method for feature selection, based on a multi-objective evolutionary algorithm, where the representation of the individuals or potential solutions, along with the bree...
![Figure 4. An illustration of one trial's timing in the Graz protocol [11].](publication/350220007/figure/fig4/AS:1004161097297922@1616422070127/An-illustration-of-one-trials-timing-in-the-Graz-protocol-11_Q320.jpg)
Motor imagery (MI) based brain–computer interface (BCI) aims to provide a means of
communication through the utilization of neural activity generated due to kinesthetic imagination of limbs. Every year, a significant number of publications that are related to new improvements, challenges, and breakthrough in MI-BCI are made. This paper provides a c...
Motor imagery (MI) electroencephalogram (EEG) signals have a low signal-to-noise ratio, which brings challenges in feature extraction and feature selection with high classification accuracy. In this study, we proposed an approach that combined an improved lasso with relief-f to extract the wavelet packet entropy features and the topological feature...
Discriminative features have to be properly extracted and selected from the electroencephalographic (EEG) signals of each specific subject in order to achieve an adaptive brain-computer interface (BCI) system. This work presents an efficient wrapper-based methodology for feature selection and least squares discrimination of high-dimensional EEG dat...
Citations
... Motor imagery-based BCI's makes it easier to boost the living standards of such physically disabled individuals. The motor cortex is triggered during a trial and creates modifications to its state [4]. Centered on this principle, researchers monitor electrical brain activity by placing a metallic electrode on the scalp for signal analysis of user intentions. ...
... The brain signals are used as commands to control external auxiliary devices (such as computer cursors on the screen, robotic arms, wheelchairs, etc.). 2,3 Mi-based BCI systems do not involve the actual movement of body parts; they help patients with severe neuromuscular disorders. 4,5 MIbased BCI is one of many brain-computer interaction control paradigms that realize the control and flow of information between the brain and the outside world by interpreting mental activity using EEG data from various MI tasks. ...
The redundant data in multichannel electroencephalogram (EEG) signals significantly reduces the performance of brain–computer interface (BCI) systems. By removing redundant channels, a channel selection strategy increases the classification accuracy of BCI systems. In this work, a novel channel selection method (stdWC) based on the standard deviation of wavelet coefficients across channels is proposed to identify Motor Imagery (MI) based EEG signals. The wavelet coefficients are calculated by employing a Continuous Wavelet Transform (CWT) filter bank to decompose each trial from the EEG channel. The wavelet coefficient's standard deviation values are obtained across the channels, and these values are then sorted to determine the EEG channels with the highest standard deviation values. The channels with the largest wavelet coefficient divergence are chosen. MI trials are then spatially filtered with the Common Spatial Pattern (CSP), and CWT filter bank‐based 2D images are generated from the spatially filtered trials. These images are then classified using a unique nine‐layered convolutional neural network (CNN) model that combines two feature maps acquired with differing filter sizes. The proposed framework (stdWC‐CSP‐CNN) is evaluated using kappa score and classification accuracy on two publically accessible datasets (BCI Competition III dataset IVa and BCI Competition IV dataset 2a). The suggested framework achieved a mean test classification accuracy of 88.8% for dataset IVa from BCI Competition III and 75.03% for dataset 2a from BCI Competition IV, according to the results. The proposed channel selection method outperforms the other channel selection methods examined, according to the results. By rejecting redundant channels, the whole framework can improve the performance of MI‐based BCIs.
... Brain-computer interface (BCI) technology enables the human brain to communicate directly with the outside world through electroencephalogram (EEG) signals and has attracted considerable attention in recent years [1]. BCIs can translate brain signals into output commands that allow users to control external auxiliary devices (such as wheelchairs, robotic arms, etc.) [2,3]. Since BCIs provide an alternative way for people to communicate without use of peripheral nerves and muscles, they show a great value of, for instance, helping patients with severe neuromuscular disorders such as spinal cord injury or amyotrophic lateral sclerosis to restore their communication pathways and control their environment [4,5]. ...
Background:
Due to the redundant information contained in multichannel electroencephalogram (EEG) signals, the classification accuracy of brain-computer interface (BCI) systems may deteriorate to a large extent. Channel selection methods can help to remove task-independent electroencephalogram (EEG) signals and hence improve the performance of BCI systems. However, in different frequency bands, brain areas associated with motor imagery are not exactly the same, which will result in the inability of traditional channel selection methods to extract effective EEG features.
New method:
To address the above problem, this paper proposes a novel method based on common spatial pattern- (CSP-) rank channel selection for multifrequency band EEG (CSP-R-MF). It combines the multiband signal decomposition filtering and the CSP-rank channel selection methods to select significant channels, and then linear discriminant analysis (LDA) was used to calculate the classification accuracy.
Results:
The results showed that our proposed CSP-R-MF method could significantly improve the average classification accuracy compared with the CSP-rank channel selection method.
... Thus, we suggest that such population-based approaches are not optimal for identifying an individual's emotional response to music or other stimuli. Therefore, we seek to develop a high performance affective state detection system as part of a hybrid BCMI (hBCMI) design, an extension of a conventional BCI [33], which incorporates both EEG features and other physiological features [34]. Additionally, we investigate personalisation of this affective state detection system. ...
Brain-computer music interfaces (BCMIs) may be used to modulate affective states, with applications in music therapy, composition, and entertainment. However, for such systems to work they need to be able to reliably detect their user's current affective state.
... Brain-computer interface (BCI) systems can translate brain activities into commands used to control external devices [1,2]. BCI systems provide a new communication method for people with severe neuromuscular disabilities, such as amyotrophic lateral sclerosis, spinal cord injury, and traumatic brain injury [3,4]. ...
... Typically, channel selection could work toward two opposite directions: (1) to select the most effective channels one by one and (2) to eliminate noisy channels one by one [30]. The sequential floating forward selection (SFFS) was originally developed by Pudil et al. [43]. ...
Background: Multichannels used in brain-computer interface (BCI) systems contain redundant information and cause inconvenience for practical application. Channel selection can enhance the performance of BCI by removing task-irrelevant and redundant channels. Sequential floating forward selection (SFFS) is an intelligent search algorithm and is considered one of the best feature selection methods in the literature. However, SFFS is time consuming when the number of features is large. Method: In this study, the SFFS method was improved to select channels for the common spatial pattern (CSP) in motor imagery (MI)-based BCI. Based on the distribution of channels in the cerebral cortex, the adjacent channels would be treated as one feature for selection. Thus, in the search process, the improved SFFS could select or remove several channels in each iteration and reduce the total computation time. Results: The improved SFFS yielded significantly better performance than using all channels (p < 0.01) and support vector machine recursive feature elimination method (p < 0.05). The computation time of the proposed method was significantly reduced (p < 0.005) compared with the original SFFS method. Conclusions: This study improved the SFFS method to select channels for CSP in MI-based BCI. The improved SFFS method could significantly reduce computation time compared with the original SFFS without compromising the classification accuracy. This study provided a way to optimize electroencephalogram. channels, which combined the distribution of channels and the intelligent selection method (SFFS). Improvements were mainly in the perspective of reducing computation time, which leads to convenience in the practical application of BCI systems.
... An example of a type of genetic algorithm being used in feature selection for BCIs is provided in (Daly et al., 2011). In this example differential evolution (a variant of a genetic algorithm) is used to identify optimal channels and frequency bands for ERD detection during a motor imagery task. ...
Brain computer Interface (BCI) development encapsulates three basic processes: data acquisition, data processing, and device control. Since the start of the millennium the BCI development cycle has undergone a metamorphosis. This is mainly due to the increased popularity of BCI applications in both commercial and research circles. One of the focuses of BCI research is to bridge the gap between laboratory research and commercial applications using this technology. A vast variety of new approaches are being employed for BCI development ranging from novel paradigms, such as simultaneous acquisitions, through to asynchronous BCI control. The strategic usage of computational techniques, comprising the heart of the BCI system, underwrites this vast range of approaches. This chapter discusses these computational strategies and translational techniques including dimensionality reduction, feature extraction, feature selection, and classification techniques.
... Recently, several methods have been described for enhancing the SNR of stimulus evoked brain responses, including SEPs, in single trials (Wang et al. 2010;Daly et al. 2011). An adaptive filtering method Lam et al. 2005), combining adaptive noise canceller and adaptive signal enhancer, was proposed to extract SEPs fast and reliably. ...
Abnormalities of somatosensory evoked potentials (SEPs) provide effective evidence for impairment of the somatosensory system, so that SEPs have been widely used in both clinical diagnosis and intraoperative neurophysiological monitoring. However, due to their low signal-to-noise ratio (SNR), SEPs are generally measured using ensemble averaging across hundreds of trials, thus unavoidably producing a tardiness of SEPs to the potential damages caused by surgical maneuvers and a loss of dynamical information of cortical processing related to somatosensory inputs. Here, we aimed to enhance the SNR of single-trial SEPs using Kalman filtering and time-frequency multiple linear regression (TF-MLR) and measure their single-trial parameters, both in the time domain and in the time-frequency domain. We first showed that, Kalman filtering and TF-MLR can effectively capture the single-trial SEP responses and provide accurate estimates of single-trial SEP parameters in the time domain and time-frequency domain, respectively. Furthermore, we identified significant correlations between the stimulus intensity and a set of indicative single-trial SEP parameters, including the correlation coefficient (between each single-trial SEPs and their average), P37 amplitude, N45 amplitude, P37-N45 amplitude, and phase value (at the zero-crossing points between P37 and N45). Finally, based on each indicative single-trial SEP parameter, we investigated the minimum number of trials required on a single-trial basis to suggest the existence of SEP responses, thus providing important information for fast SEP extraction in intraoperative monitoring.
... In motor imagery-based BCI studies, participants are asked to imagine experiment-related tasks. During a trial, the motor cortex is activated and produces changes of its state [9,10]. Based on this theory, researchers record electrical brain activity by placing electrodes on the scalp or with subdural electrodes and analysing the electrical signals (EEG or electrocorticogram (ECoG)) to determine their intents. ...
Motivation of subject who associated electroencephalogram based brain computer interface experiments is one of the most important parameters which affect this kind of research's performance. Researchers can not able to measure how the motivation of subject exactly changes during the experiment. The proposed method was successfully applied to the BCI Competition 2003 Data Set III by using discrete Fourier Transform features with linear discriminant analysis classifier. The results showed that while the motivation of subject was very low level at first 0.5 sec, it increased after that time rapidly and again decreased after 3.25 sec on 6 second long of a trial.
... The development of EEG-based interpreting approaches is an interesting application which makes real-time decoding systems possible (Muller et al. 2008). In order to decode the EEG-based tasks, three main aspects namely feature extraction (Sykacek et al. 2003;Ince et al. 2005;Wang et al. 2010), feature selection (Pregenzer and Pfurtscheller 1999;Garrett et al. 2003;Lal et al. 2004;Daly et al. 2011;Long et al. 2010) and classification approaches (Palaniappan et al. 2002;Peters et al. 2001) can be considered to analyze EEG signals (Coyle et al. 2005;Wolpaw et al. 2002;Coyle et al. 2006a;2006b). The EEG spectrum is normally composed of five different frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz) and gamma (from 30 HZ). ...
... The development of EEG-based interpreting approaches is an interesting application which makes real-time decoding systems possible (Muller et al. 2008). In order to decode the EEG-based tasks, three main aspects namely feature extraction (Sykacek et al. 2003;Ince et al. 2005;Wang et al. 2010), feature selection (Pregenzer and Pfurtscheller 1999;Garrett et al. 2003;Lal et al. 2004;Daly et al. 2011;Long et al. 2010) and classification approaches (Palaniappan et al. 2002;Peters et al. 2001) can be considered to analyze EEG signals (Coyle et al. 2005;Wolpaw et al. 2002;Coyle et al. 2006a;2006b). The EEG spectrum is normally composed of five different frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz) and gamma (from 30 HZ). ...
In this article a quantitative analysis was devised assessing driver's cognition responses by exploring the neurobiological information underlying electroencephalographic (EEG) brain signals in a left and right turning experiment on simulator environment. Driving brain signals have been collected by a 19-channel electroencephalogram recording system. The driving pathway has been selected with no obstacles, a set of indicators are used to inform the subjects when they had to turn left or right by means of keyboard left and right arrows. Subsequently in order to remove artifacts, preprocessing is performed on data to achieve high accuracy. Features of signals are extracted by using Fast Fourier Transform (FFT). Absolute power of FFT is used as a basic feature. Scalar Feature selection method is applied to reduce feature dimension. Thereafter dimension-reduced features are fed to Hopfield Neural Network (HNN) recognizing different brain potentials stimulated by turning to left and right. The performances of HNN are evaluated by considering five conditions; before feature extraction, after feature extraction, before reduction of features, after analyzing reduced features and finally subject-wise Hopfield performances respectively. An increase occurred in each level and continued until it has reached its highest 97.6% of accuracy on last condition.
