Toward enhanced P300 speller performance

doi:10.1016/j.jneumeth.2007.07.017

Journal of Neuroscience Methods

Volume 167, Issue 1, 15 January 2008, Pages 15-21

https://doi.org/10.1016/j.jneumeth.2007.07.017 Get rights and content

Abstract

This study examines the effects of expanding the classical P300 feature space on the classification performance of data collected from a P300 speller paradigm [Farwell LA, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroenceph Clin Neurophysiol 1988;70:510–23]. Using stepwise linear discriminant analysis (SWLDA) to construct a classifier, the effects of spatial channel selection, channel referencing, data decimation, and maximum number of model features are compared with the intent of establishing a baseline not only for the SWLDA classifier, but for related P300 speller classification methods in general. By supplementing the classical P300 recording locations with posterior locations, online classification performance of P300 speller responses can be significantly improved using SWLDA and the favorable parameters derived from the offline comparative analysis.

Introduction

A brain–computer interface (BCI) is a device that uses brain signals to provide a non-muscular communication channel (Wolpaw et al., 2002), particularly for individuals with severe neuromuscular disabilities. The P300-event related potential is an evoked response to an external stimulus that is observed in scalp-recorded electroencephalography (EEG). The P300 response has proven to be a reliable signal for controlling a BCI (Farwell and Donchin, 1988). Farwell and Donchin (1988) describe the P300 speller, which presents a selection of characters arranged in a 6 × 6 matrix. The user focuses attention on one of the 36 character cells of the matrix while each row and column of the matrix is intensified in a random sequence. The row and column intensifications that intersect at the attended cell represent the target stimuli, which occur with a probability of 1/6. The rare presentation of the target stimuli in the random sequence of stimuli constitutes an Oddball Paradigm (Fabiani et al., 1987) and will elicit a P300 response to the target stimuli. With proper P300 feature selection and classification, the attended character of the matrix can be identified and communicated.

A variety of feature extraction and classification procedures such as stepwise linear discriminate analysis (SWLDA) (Donchin et al., 2000, Sellers and Donchin, 2006), wavelets (Bostanov, 2004), support vector machines (Kaper et al., 2004, Meinicke et al., 2002, Thulasidas et al., 2006), and matched filtering (Serby et al., 2005) have been implemented, improving the performance beyond that originally reported in (Farwell and Donchin, 1988). Based on multiple studies in healthy volunteers (Donchin et al., 2000, Sellers and Donchin, 2006, Serby et al., 2005), and initial studies in persons with physical disability (Vaughan et al., 2006), the P300 speller has potential to serve as an effective communication device for persons who have lost or are losing the ability to write and speak. An individual with advanced-stage ALS reported the P300 speller to be superior and preferential to his modern eye-gaze system and uses the BCI 4–6 h/day for e-mail and other computer applications (Vaughan et al., 2006). Initial reports from other disabled individuals currently testing the P300 speller BCI also indicate that its speed, accuracy, and ease of use are superior or competitive with other assistive technologies.

Up to the present, BCI-related P300 research has focused almost exclusively on signals from standard P300 scalp locations (i.e., Fz, Cz, Pz). While recent offline evaluations suggest that the use of additional locations, particularly posterior sites, may improve classification accuracy (BCI, 2003, BCI, 2005, Blankertz et al., 2004, Blankertz et al., 2006, Kaper et al., 2004, Spencer et al., 2001, Vaughan et al., 2003), this possibility has not been formally addressed in comprehensive offline and online studies.

To address this possibility, the present study explores the value of incorporating information from electrode locations that are not traditionally associated with the P300 response. In addition, several data preprocessing and model parameters are evaluated to assess the relative effects with respect to the new spatial information. Using a SWLDA classifier, both offline and online results obtained from 64-channel data show that some of the most discriminable EEG features evoked by the P300 speller occur at posterior electrodes (namely PO₇, PO₈, Oz), and that these features can significantly improve classification performance when used in conjunction with the classical P300 feature space (i.e., EEG features at electrodes Fz, Cz, Pz (Sharbrough et al., 1991)). The results are also relevant to current speculations concerning the nature of the neural processes underlying P300-based BCI operation and the question of their dependence on gaze direction.

Section snippets

Participants

Seven able-bodied people were the participants in this study. The demographics and previous speller matrix experience of the participants are listed in Table 1. The participants varied in their previous BCI experience, but all participants had either no experience or relatively few sessions with a P300-based BCI system. The study was approved by the New York State Department of Health Institutional Review Board, and each participant gave informed consent.

Task, procedure, and design

The participant sat upright in front of

Stepwise linear discriminant analysis

Determining the presence or absence of a P300 evoked potential from EEG features can be considered a binary classification problem with a decision hyper-plane defined by: $w \cdot x - b = 0$ where x is the feature vector as described in Section 2.2, w a vector of feature weights, and b is the bias term. However, because it is assumed that a P300 is elicited for one of the six row/column intensifications, and that the initial results indicate that the P300 response is invariant to row/column stimuli, the

Analysis protocol

In the previous work on SWLDA for classifying P300 responses (BCI, 2003, Farwell and Donchin, 1988, Sellers and Donchin, 2006), only channels Fz, Cz, and Pz were used for analysis. However, the posterior response seems to provide significant additional discriminative information for the P300 speller (BCI, 2003, BCI, 2005, Blankertz et al., 2004, Blankertz et al., 2006, Kaper et al., 2004, Spencer et al., 2001, Vaughan et al., 2003). Thus far, neither the temporal attributes of this posterior

Offline evaluation

The four factors of primary interest were examined separately offline. Fig. 3 illustrates the fundamental effects of channel set, reference, decimation, and maximum features, respectively. Because all combinations of factors were initially evaluated, factors that are clearly superior or show consistent performance across conditions are fixed for presentation purposes. For example, the factors of decimation and maximum features do not result in significantly different performance for any of the

Discussion

It is evident from the results that an essential factor in optimizing the performance of the P300 classifier is the identification of an appropriate channel set for the individual. Channel set was the only factor that yielded a significant statistical effect on classification rates; however, the analyses of the other factors have provided important information. The analyses conducted here suggest that, on average, a decimation of 12, ear reference, maximum features of at least 45, and either

Acknowledgements

This work was supported in part by the National Institutes of Health under Grant NICHD HD30146 and Grant NIBIB/NINDS EB00856 and in part by the James S. McDonnell Foundation.

References (26)

L.A. Farwell et al.
Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials
Electroenceph Clin Neurophysiol
(1988)
J. Intriligator et al.
On the relationship between background EEG and the P300 event-related potential
Biol Psychol
(1994)
A.C. Nobre et al.
The dynamics of shifting visuospatial attention revealed by event-related potentials
Neuropsychologia
(2000)
E.W. Sellers et al.
A P300-based brain–computer interface: initial tests by ALS patients
Clin Neurophysiol
(2006)
J.R. Wolpaw et al.
Brain–computer interfaces for communication and control
Clin Neurophysiol
(2002)
BCI Competition II Data Set IIb. Results (Blankertz and Curio); (2003)...
BCI Competition III. Data Set II Results; (2005)...
B. Blankertz et al.
The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials
IEEE Trans Biomed Eng
(2004)
B. Blankertz et al.
The BCI competition III: validating alternative approaches to actual BCI problems
IEEE Trans Neural Syst Rehabil Eng
(2006)
V. Bostanov
BCI competition 2003-data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram
IEEE Trans Biomed Eng
(2004)

E. Donchin et al.

The mental prosthesis: assessing the speed of a P300-based brain–computer interface

IEEE Trans Rehabil Eng

(2000)

N. Draper et al.

Applied regression analysis

(1981)

M. Fabiani et al.

Definition, identification, and reliability of measurement of the P300 component of the event-related brain potential

Adv Psychophysiol

(1987)

Cited by (623)

Compact temporal dilated convolution with Channel-wise attention and cost sensitive learning for Single trial P300 detection
2023, Biomedical Signal Processing and Control
The P300 speller is a challenging task due to various factors, such as morphological and temporal variabilities, noisy channels, imbalanced P300/non-P300 data, numerous trainable parameters, and lengthy spelling cycles. To overcome these issues, we propose a novel solution that incorporates temporal dilated convolution with channel-wise attention into a lightweight base classifier and employs cost-sensitive learning to handle data imbalance. We evaluate our system through extensive subject-dependent and cross-subject experiments on two standard datasets: Amyotrophic Lateral Sclerosis (ALS) and Devanagari Script (DS).
Our results demonstrate a significant improvement in P300 classification with a 2–3 times reduction in trainable parameters compared to single-trial experiments. Our proposed model outperforms state-of-the-art compact models and offers better trade-off between computational complexity and classification performance.
Our approach offers new insights into the challenges of P300 classification and provides inspiration for future research in this field. We are confident that our proposed model will contribute to the advancement of P300 classification and benefit the scientific community.
Identifying markers volatiles in Brazilian virgin oil by multiple headspace solid-phase microextraction, and chemometrics tools
2023, Food Research International
A protocol was optimized to determine the volatile profile from monovarietal virgin olive oil (VOO) by multiple headspace solid-phase microextraction (MHS-SPME) followed by gas chromatography-mass spectrometry (GC–MS) analysis. For this, a Plackett-Burman (PB) and central composite rotational designs (CCRD) were used to define the best condition of extraction. Moreover, fatty acids profile and principal component analysis (PCA) was used to identify markers among the cultivars. The amount of 0.1 g of sample was enough to express the volatile composition of the olive oils by MHS-SPME. Volatile compounds [nonanal, (Z)-3-Hexen-1-ol, (Z)-3-Hexenyl Acetate, Hexyl Acetate, 3-Methylbutyl Acetate, (E)-2-Hexen-1-ol, (E)-2-Hexenyl Acetate] and fatty acids [C17:1, C18, C18:1, C18:2] were those reported such as the markers in the varieties of olive oils. The PCA analysis allowed the classification of the most representative volatiles and fatty acids for each cultivar. Through two principal components was possible to obtain 81.9% of explanation of the variance of the compounds. The compounds were quantified using a validated method. The MHS-SPME combined with multivariate analysis showed a promising tool to identify markers and for the discrimination of olive oil varieties.
A new benchmark dataset for P300 ERP-based BCI applications
2023, Digital Signal Processing: A Review Journal
Because of its non-invasive nature, one of the most commonly used event-related potentials in brain-computer interface (BCI) system designs is the P300 electroencephalogram (EEG) signal. The fact that the P300 response can easily be stimulated and measured is particularly important for participants with severe motor disabilities. In order to train and test P300-based BCI speller systems in more realistic high-speed settings, there is a pressing need for a large and challenging benchmark dataset. Various datasets already exist in the literature but most of them are not publicly available, and they either have a limited number of participants or utilize relatively long stimulus duration (SD) and inter-stimulus intervals (ISI). They are also typically based on a 36 target ( $6 \times 6$ ) character matrix. The use of long ISI, in particular, not only reduces the speed and the information transfer rates (ITRs) but also oversimplifies the P300 detection. This leaves a limited challenge to state-of-the-art machine learning and signal processing algorithms. In fact, near-perfect P300 classification accuracies are reported with the existing datasets. Therefore, one certainly needs a large-scale dataset with challenging settings to fully exploit the recent advancements in algorithm design (machine learning and signal processing) and achieve high-performance speller results. To this end, in this article we introduce a new freely- and publicly-accessible P300 dataset obtained using 32-channel EEG, in the hope that it will lead to new research findings and eventually more efficient BCI designs. The introduced dataset comprises 18 participants performing a 40-target ( $5 \times 8$ ) cued-spelling task, with reduced SD (66.6 ms) and ISI (33.3 ms) for fast spelling. We have also processed, analyzed, and character-classified the introduced dataset and we presented the accuracy and ITR results as a benchmark. The introduced dataset and the codes of our experiments are publicly accessible at https://data.mendeley.com/datasets/vyczny2r4w.
A mutli-scale spatial-temporal convolutional neural network with contrastive learning for motor imagery EEG classification
2023, Medicine in Novel Technology and Devices
Motor imagery (MI) based Brain-computer interfaces (BCIs) have a wide range of applications in the stroke rehabilitation field. However, due to the low signal-to-noise ratio and high cross-subject variation of the electroencephalogram (EEG) signals generated by motor imagery, the classification performance of the existing methods still needs to be improved to meet the need of real practice. To overcome this problem, we propose a multi-scale spatial-temporal convolutional neural network called MSCNet. We introduce the contrastive learning into a multi-temporal convolution scale backbone to further improve the robustness and discrimination of embedding vectors. Experimental results of binary classification show that MSCNet outperforms the state-of-the-art methods, achieving accuracy improvement of 6.04%, 3.98%, and 8.15% on BCIC IV 2a, SMR-BCI, and OpenBMI datasets in subject-dependent manner, respectively. The results show that the contrastive learning method can significantly improve the classification accuracy of motor imagery EEG signals, which provides an important reference for the design of motor imagery classification algorithms.
A dynamical graph-based feature extraction approach to enhance mental task classification in brain–computer interfaces
2023, Computers in Biology and Medicine
Graph theoretic approaches in analyzing spatiotemporal dynamics of brain activities are under-studied but could be very promising directions in developing effective brain–computer interfaces (BCIs). Many existing BCI systems use electroencephalogram (EEG) signals to record and decode human neural activities noninvasively. Often, however, the features extracted from the EEG signals ignore the topological information hidden in the EEG temporal dynamics. Moreover, existing graph theoretic approaches are mostly used to reveal the topological patterns of brain functional networks based on synchronization between signals from distinctive spatial regions, instead of interdependence between states at different timestamps. In this study, we present a robust fold-wise hyperparameter optimization framework utilizing a series of conventional graph-based measurements combined with spectral graph features and investigate its discriminative performance on classification of a designed mental task in 6 participants with amyotrophic lateral sclerosis (ALS). Across all of our participants, we reached an average accuracy of $71.1 % \pm 4.5 %$ for mental task classification by combining the global graph-based measurements and the spectral graph features, higher than the conventional non-graph based feature performance ( $67.1 % \pm 7.5 %$ ). Compared to using either one of the graphic features ( $66.3 % \pm 6.5 %$ for the eigenvalues and $65.9 % \pm 5.2 %$ for the global graph features), our feature combination strategy shows considerable improvement in both accuracy and robustness performance. Our results indicate the feasibility and advantage of the presented fold-wise optimization framework utilizing graph-based features in BCI systems targeted at end-users.
The Effect of Jittered Stimulus Onset Interval on Electrophysiological Markers of Attention in a Brain–Computer Interface Rapid Serial Visual Presentation Paradigm
2024, Signals

View all citing articles on Scopus

View full text

Toward enhanced P300 speller performance

Abstract

Introduction

Section snippets

Participants

Task, procedure, and design

Stepwise linear discriminant analysis

Analysis protocol

Offline evaluation

Discussion

Acknowledgements

Electroenceph Clin Neurophysiol

Biol Psychol

Neuropsychologia

Clin Neurophysiol

Clin Neurophysiol

The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials

IEEE Trans Biomed Eng

The BCI competition III: validating alternative approaches to actual BCI problems

IEEE Trans Neural Syst Rehabil Eng