A Variance-based Approach to Perform Single-Trial P300 Detection (original) (raw)
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A boosting approach to P300 detection with application to brain-computer interfaces
2005
Abstract Gradient boosting is a machine learning method, that builds one strong classifier from many weak classifiers. In this work, an algorithm based on gradient boosting is presented, that detects event-related potentials in single electroencephalogram (EEG) trials. The algorithm is used to detect the P300 in the human EEG and to build a brain-computer interface (BCI), specifically a spelling device. Important features of the method described here are its high classification accuracy and its conceptual simplicity.
American Journal of Applied Sciences, 2010
The P300 component of Event Related Brain Potentials (ERP) is commonly used in Brain Computer Interfaces (BCI) to translate the intentions of an individual into commands for external devices. The P300 response, however, resides in a signal environment of high background noise. Consequently, the main problem in developing a P300-based BCI lies in identifying the P300 response in the presence of this noise. Traditionally, attenuating the background activity of P300 data is done by averaging multiple trials of recorded signals. This method, though effective, suffers two drawbacks. First, collecting multiple trials of data is time consuming and delays the BCI response. Second, latency distortions may appear in the averaged result due to variable time-locking of the P300 in the individual trials. Problem statement: The use of single-trial P300 data overcomes both these shortcomings. However, single-trial data must be properly denoised to allow for reliable BCI operation. Single-trial P300-based BCIs have been implemented using a variety of signal processing techniques and classification methodologies. However, comparing the accuracies of these systems to other multi-trial systems is likely to include the comparison of more than just the trial format (single-trial/multi-trial) as the data quality and recording circumstances are likely to be dissimilar. Approach: This issue was directly addressed by comparing the performance comparison of three different preprocessing agents and three classification methodologies on the same data set over both the single-trial and multi-trial settings. The P300 data set of BCI Competition II was used to facilitate this comparison. Results: The LDA classifier exhibited the best performance in classifying unseen P300 spatiotemporal features in both the single-trial (74.19%) and multi-trial format (100%). It is also very efficient in terms of computational and memory requirements. Conclusion: This study can serve as a general guide for practitioners developing single-trial and multi-trial P300-based BCI systems, particularly for selecting appropriate pre-processing agents and classification methodologies for inclusion. The possibilities for future study include the investigation of double-trial and triple-trial P300 system based on the LDA classifier. The time savings of such approaches will still be significant. It is very likely that such systems would benefit from accuracies higher than the one obtained in this study for single-trial LDA (74.19%).
P300 detection based on Feature Extraction in on-line Brain-Computer Interface
Chumerin.2009-KI-LNCS, 2009
We propose a new EEG-based wireless brain computer interface (BCI) with which subjects can “mind-type” text on a computer screen. The application is based on detecting P300 event-related potentials in EEG signals recorded on the scalp of the subject. The BCI uses a simple classifier which relies on a linear feature extraction approach. The accuracy of the presented system is comparable to the state-of-the-art for on-line P300 detection, but with the additional benefit that its much simpler design supports a power-efficient on-chip implementation.
An application of feature selection to on-line P300 detection in brain-computer interface
Chumerin.2009-MLSP, 2009
We propose a new EEG-based wireless brain computer interface (BCI) with which subjects can ldquomind-typerdquo text on a computer screen. The application is based on detecting P300 event-related potentials in EEG signals recorded on the scalp of the subject. The BCI uses a linear classifier which takes as input a set of simple amplitude-based features that are optimally selected using the group method of data handling (GMDH) feature selection procedure. The accuracy of the presented system is comparable to the state-of-the-art systems for on-line P300 detection, but with the additional benefit that its much simpler design supports a power-efficient on-chip implementation.
Design, Implementation and Evaluation of a Real-Time P300-based Brain-Computer Interface System
2010 20th International Conference on Pattern Recognition, 2010
We present a new end-to-end brain-computer interface system based on electroencephalography (EEG). Our system exploits the P300 signal in the brain, a positive deflection in event-related potentials, caused by rare events. P300 can be used for various tasks, perhaps the most wellknown being a spelling device. We have designed a flexible visual stimulus mechanism that can be adapted to user preferences and developed and implemented EEG signal processing, learning and classification algorithms. Our classifier is based on Bayes linear discriminant analysis, in which we have explored various choices and improvements. We have designed data collection experiments for offline and online decision-making and have proposed modifications in the stimulus and decision-making procedure to increase online efficiency. We have evaluated the performance of our system on 8 healthy subjects on a spelling task and have observed that our system achieves higher average speed than state-of-the-art systems reported in the literature for a given classification accuracy.
EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces
Brain Sciences
The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition.
A robust sensor-selection method for P300 brain-computer interfaces
Journal of Neural Engineering, 2011
A brain-computer interface (BCI) is a specific type of human-computer interface that enables direct communication between human and computer through decoding of brain activity. As such, event-related potentials like the P300 can be obtained with an oddball paradigm whose targets are selected by the user. This paper deals with methods to reduce the needed set of EEG sensors in the P300 speller application. A reduced number of sensors yields more comfort for the user, decreases installation time duration, may substantially reduce the financial cost of the BCI setup and may reduce the power consumption for wireless EEG caps. Our new approach to select relevant sensors is based on backward elimination using a cost function based on the signal to signal-plus-noise ratio, after some spatial filtering. We show that this cost function selects sensors' subsets that provide a better accuracy in the speller recognition rate during the test sessions than selected subsets based on classification accuracy. We validate our selection strategy on data from 20 healthy subjects.
Advances and Challenges in Signal Analysis for Single Trial P300-BCI
Lecture Notes in Computer Science, 2011
In this paper a brief introduction to some of the goals, recent developments, and open problems in BCI research are given. We mainly focus on presenting our research work in signal processing for single-trial P300-BCI and discuss our current plans for improving the BCI method.
Investigation of New Unsupervised Processing Methods for P300-Based Brain-Computer Interface
Journal of Medical Imaging and Health Informatics, 2014
In Brain-computer Interface (BCI), the detection of activations is based on the experience gained through calibration or training sessions prior to actual use to build the classification model. This gives rise to several problems that include inter-session variability and time fading of accuracy after calibration. In this work, we investigate a new approach for brain-computer interface data that requires no prior training. The basic principle of this new class of unsupervised techniques is that the trial with true activation signal within each block has to be different from the rest of the trials within that block. Hence, a measure that is sensitive to this dissimilarity can be used to make a decision based on a single block without any prior training. The new approach is applied to experimental data for P300-based BCI for both normal and disabled subjects and compared to the classification results of the same data using the conventional processing techniques requiring prior calibration. Performance in different experiments assessed using classification block accuracy suggests that this approach can reach accuracies not very far from those obtained with training while maintaining robust performance in practice.
A single channel-single trial P300 detection algorithm
2013 21st Iranian Conference on Electrical Engineering (ICEE), 2013
A Brain Computer Interface (BCI) system allows the users to communicate with their surroundings without using any muscle activity. Many of these systems are based on the analysis of Event Related Potentials (ERPs) such as P300. P300 speller is one of the common BCI systems which attract a lot of attention; however, there are still a lot of flaws in these systems which should be considered. Since ERPs such as P300 signals have a very low Signal to Noise Ratio (SNR), single trial analysis of these signals is difficult and in many papers, denoising methods such as synchronous averaging were proposed to reduce random noise; however, it reduces the communication rate greatly. Another major problem in many BCI applications is the numerous number of channels needed to record EEG signals in order to have a reliable system. In this paper, a new method is presented to detect P300 signals through single channel data analysis and also it reaches an average accuracy of 65% in single trial P300 detection. I.