Advances and Challenges in Signal Analysis for Single Trial P300-BCI

Li, Kun; Raju, Vanitha Narayan; Sankar, Ravi; Arbel, Yael; Donchin, Emanuel

doi:10.1007/978-3-642-21852-1_11

Cited by 5 publications

(7 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is worth noting that for Dataset-1 the average classification result were over 77% and for Dataset-2 were over 75% for all KLSDA configuration. These results are very encouraging since an efficient BCI system requires of an accuracy above 70% to allow communication and device control [24].…”

Section: Classification Resultsmentioning

confidence: 69%

“…Several authors have investigated in regards to which classification technique performs best for separating target from non-target EEG records [25,4,24,11]. All of these studies have concluded that linear discriminant anal-ysis (LDA) is a very good classification scheme, resulting most of the times in the optimal performance while keeping the solution simple.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Generalized sparse discriminant analysis for event-related potential classification

Peterson

Rufiner

Spies

2017

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

A brain computer interface (BCI) is a system which provides direct communication between the mind of a person and the outside world by using only brain activity (EEG). The event-related potential (ERP)-based BCI problem consists of a binary pattern recognition. Linear discriminant analysis (LDA) is widely used to solve this type of classification problems, but it fails when the number of features is large relative to the number of observations. In this work * we propose a penalized version of the sparse discriminant analysis (SDA), called Kullback-Leibler penalized sparse discriminant analysis (KLSDA). This method inherits both the discriminative feature selection and classification properties of SDA and it also improves SDA performance through the addition of Kullback-Leibler class discrepancy information. The KLSDA method is design to automatically select the optimal regularization parameters. Numerical experiments with two real ERP-EEG datasets show that this new method outperforms standard SDA.

show abstract

Section: Classification Resultsmentioning

confidence: 69%

Section: Introductionmentioning

confidence: 99%

Generalized sparse discriminant analysis for event-related potential classification

Peterson

Rufiner

Spies

2017

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

show abstract

“…The last few decades have seen a lot of research on P300 classification, with the eventual aim of achieving single-trial based reliable P300 BCI [21]. But a literature survey of this research does not show any single P300 classification method to be the state of the art.…”

Section: Related Workmentioning

confidence: 95%

“…Types of BCIs can be broadly classified into two categories-those that use an external stimulus, and those that don't [21]. In the first method, the external stimuli cause changes in neurophysiologic signals called event-related potentials (ERPs) [15,22] which are used to identify a user's response to the stimuli presented.…”

Section: Bci and P300mentioning

confidence: 99%

“…A brain-computer interface (BCI) is a device that uses brain signals to provide a direct, nonmuscular communication channel between brain and the outside world [32,31,29]. The idea underlying BCIs is to measure electric, magnetic, or other physical manifestations of the brain activity and to translate these into commands for a computer or other devices [21,15].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Single-trial P300 Classification using PCA with LDA, QDA and Neural Networks

Sharma¹

2017

Preprint

View full text Add to dashboard Cite

The P300 event-related potential (ERP), evoked in scalp-recorded electroencephalography (EEG) by external stimuli, has proven to be a reliable response for controlling a BCI. The P300 component of an event related potential is thus widely used in brain-computer interfaces to translate the subjects' intent by mere thoughts into commands to control artificial devices. The main challenge in the classification of P300 trials in electroencephalographic (EEG) data is the low signal-to-noise ratio (SNR) of the P300 response. To overcome the low SNR of individual trials, it is common practice to average together many consecutive trials, which effectively diminishes the random noise. Unfortunately, when more repeated trials are required for applications such as the P300 speller, the communication rate is greatly reduced. This has resulted in a need for better methods to improve single-trial classification accuracy of P300 response. In this work, we use Principal Component Analysis (PCA) as a preprocessing method and use Linear Discriminant Analysis (LDA)and neural networks for classification. The results show that a combination of PCA with these methods provided as high as 13% accuracy gain for single-trial classification while using only 3 to 4 principal components.

show abstract