Stimulus modality influences session-to-session transfer of training effects in auditory and tactile streaming-based P300 brain–computer interfaces

Despite recent successes, patients suffering from locked-in syndrome (LIS) still struggle to communicate using vision-independent brain–computer interfaces (BCIs). In this study, we compared auditory and tactile BCIs, regarding training effects and cross-stimulus-modality transfer effects, when switching between stimulus modalities. We utilized a streaming-based P300 BCI, which was developed as a low workload approach to prevent potential BCI-inefficiency. We randomly assigned 20 healthy participants to two groups. The participants received three sessions of training either using an auditory BCI or using a tactile BCI. In an additional fourth session, BCI versions were switched to explore possible cross-stimulus-modality transfer effects. Both BCI versions could be operated successfully in the first session by the majority of the participants, with the tactile BCI being experienced as more intuitive. Significant training effects were found mostly in the auditory BCI group and strong evidence for a cross-stimulus-modality transfer occurred for the auditory training group that switched to the tactile version but not vice versa. All participants were able to control at least one BCI version, suggesting that the investigated paradigms are generally feasible and merit further research into their applicability with LIS end-users. Individual preferences regarding stimulus modality should be considered

A Generic Transferable EEG Decoder for Online Detection of Error Potential in Target Selection

Reliable detection of error from electroencephalography (EEG) signals as feedback while performing a discrete target selection task across sessions and subjects has a huge scope in real-time rehabilitative application of Brain-computer Interfacing (BCI). Error Related Potentials (ErrP) are EEG signals which occur when the participant observes an erroneous feedback from the system. ErrP holds significance in such closed-loop system, as BCI is prone to error and we need an effective method of systematic error detection as feedback for correction. In this paper, we have proposed a novel scheme for online detection of error feedback directly from the EEG signal in a transferable environment (i.e., across sessions and across subjects). For this purpose, we have used a P300-speller dataset available on a BCI competition website. The task involves the subject to select a letter of a word which is followed by a feedback period. The feedback period displays the letter selected and, if the selection is wrong, the subject perceives it by the generation of ErrP signal. Our proposed system is designed to detect ErrP present in the EEG from new independent datasets, not involved in its training. Thus, the decoder is trained using EEG features of 16 subjects for single-trial classification and tested on 10 independent subjects. The decoder designed for this task is an ensemble of linear discriminant analysis, quadratic discriminant analysis, and logistic regression classifier. The performance of the decoder is evaluated using accuracy, F1-score, and Area Under the Curve metric and the results obtained is 73.97, 83.53, and 73.18%, respectively

Reducing BCI calibration time with transfer learning: a shrinkage approach

Introduction: A brain-computer interface system (BCI) allows subjects to make use of neural control signals to drive a computer application. Therefor a BCI is generally equipped with a decoder to differentiate between types of responses recorded in the brain. For example, an application giving feedback to the user can benefit from recognizing the presence or absence of a so-called error potential (Errp), elicited in the brain of the user when this feedback is perceived as being ‘wrong’, a mistake of the system. Due to the high inter- and intra- subject variability in these response signals, calibration data needs to be recorded to train the decoder. This calibration session is exhausting and demotivating for the subject. Transfer Learning is a general name for techniques in which data from previous subjects is used as additional information to train a decoder for a new subject, thereby reducing the amount of subject specific data that needs to be recorded during calibration. In this work we apply transfer learning to an Errp detection task by applying single-target shrinkage to Linear Discriminant Analysis (LDA), a method originally proposed by Höhne et. al. to improve accuracy by compensating for inter-stimuli differences in an ERP-speller [1]. Material, Methods and Results: For our study we used the error potential dataset recorded by Perrin et al. in [2]. For 26 subjects each, 340 Errp/nonErrp responses were recorded with a #Errp to #nonErrp ratio of 0.41 to 0.94. 272 responses were available for training the decoder and the remaining 68 responses were left out for testing. For every subject separately we built three different decoders. First, a subject specific LDA decoder was built solely making use of the subject’s own train data. Second, we added the train data of the other 25 subjects to train a global LDA decoder, naively ignoring the difference between subjects. Finally, the single-target-shrinkage method (STS) [1] is used to regularize the parameters of the subject specific decoder towards those of the global decoder. Making use of cross validation this method assigns an optimal weight to the subject specific data and data from previous subjects to be used for training. Figure 1 shows the performance of the three decoders on the test data in terms of AUC as a function of the amount of subject specific calibration data used. Discussion: The subject specific decoder in Figure 1 shows how sensitive the decoding performance is to the amount of calibration data provided. Using data from previously recorded subjects the amount of calibration data, and as such the calibration time, can be reduced as shown by the global decoder. A certain amount of quality is however sacrificed. Making an optimal compromise between the subject specific and global decoder, the single-target-shrinkage decoder allows the calibration time to be reduced by 20% without any change in decoder quality (confirmed by a paired sample t-test giving p=0.72). Significance: This work serves as a first proof of concept in the use of shrinkage LDA as a transfer learning method. More specific, the error potential decoder built with reduced calibration time boosts the opportunity for error correcting methods in BCI

Evaluation of flashing stimuli shape and colour heterogeneity using a P300 brain-computer interface speller

Objective: Previous works using a visual P300-based speller have reported an improvement modifying the shape or colour of the presented stimulus. However, the effects of both blended factors have not been yet studied. Thus, the aim of the present work was to study both factors and assess the interaction between them. Method: Fifteen na\"ive participants tested four different spellers in a calibration and online task. All spellers were similar except the employed illumination of the target stimulus: white letters, white blocks, coloured letters, and coloured blocks. Results: The block-shaped conditions offered an improvement versus the letter-shaped conditions in the calibration (accuracy) and online (accuracy and correct commands per minute) tasks. Analysis of the P300 waveform showed a larger difference between target and no target stimulus waveforms for the block-shaped conditions versus the letter-shaped. The hypothesis regarding the colour heterogeneity of the stimuli was not found at any level of the analysis. Conclusion: The use of block-shaped illumination demonstrated a better performance than the standard letter-shaped flashing stimuli in classification performance, correct commands per minute, and P300 waveform

Manipulating Paradigm and Attention via a Mindfulness Meditation Training Program Improves P300-Based BCI.

To date, only one study has situationally bolstered attentional resources in an effort to improve P300-BCI performance. The current study implements a 4-week Mindfulness Meditation Training Program (MMTP) as a nonmedicinal means to increase concentrative attention and to reduce lapses of attention; MMTP is expected to improve P300-BCI performance by enhancing attentional resources and reducing distractibility. A second aim is to test the efficacy of the checkerboard paradigm (CBP) against the standard row-column paradigm (RCP). Online results show that MMTP had greater accuracies than CTRL and that CBP outperformed the RCP. MMTP participants provided greater amplitude positive target responses, but these differences were not statistically significant. CBP had greater positive amplitude peaks and negative peaks than RCP. The discussion focuses on potential benefits of MMTP for P300-based BCIs, provides further support for the construct validity of mindfulness, and addresses future directions of the translational applicability of MMTP to in-home settings

Comparison of Two Paradigms Based on Stimulation with Images in a Spelling Brain–Computer Interface

A P300-based speller can be used to control a home automation system via brain activity. Evaluation of the visual stimuli used in a P300-based speller is a common topic in the field of brain–computer interfaces (BCIs). The aim of the present work is to compare, using the usability approach, two types of stimuli that have provided high performance in previous studies. Twelve participants controlled a BCI under two conditions, which varied in terms of the type of stimulus employed: a red famous face surrounded by a white rectangle (RFW) and a range of neutral pictures (NPs). The usability approach included variables related to effectiveness (accuracy and information transfer rate), efficiency (stress and fatigue), and satisfaction (pleasantness and System Usability Scale and Affect Grid questionnaires). The results indicated that there were no significant differences in effectiveness, but the system that used NPs was reported as significantly more pleasant. Hence, since satisfaction variables should also be considered in systems that potential users are likely to employ regularly, the use of different NPs may be a more suitable option than the use of a single RFW for the development of a home automation system based on a visual P300-based speller.This work was partially supported by the project PID2021-127261OB-I00 (SICODIS), funded by MCIN (Ministerio de Ciencia e Innovación)/AEI (Agencia Estatal de Investigación)/10.13039/501100011033/FEDER, UE (Fondo Europeo de Desarrollo Regional). The work was also partially supported by the University of Málaga (Universidad de Málaga). Partial funding for open access charge: Universidad de Málag

Optimizing P300-speller sequences by RIP-ping groups apart

International audienceSo far P300-speller design has put very little emphasis on the design of optimized flash patterns, a surprising fact given the importance of the sequence of flashes on the selection outcome. Previous work in this domain has consisted in studying consecutive flashes, to prevent the same letter or its neighbors from flashing consecutively. To this effect, the flashing letters form more random groups than the original row-column sequences for the P300 paradigm, but the groups remain fixed across repetitions. This has several important consequences, among which a lack of discrepancy between the scores of the different letters. The new approach proposed in this paper accumulates evidence for individual elements, and optimizes the sequences by relaxing the constraint that letters should belong to fixed groups across repetitions. The method is inspired by the theory of Restricted Isometry Property matrices in Compressed Sensing, and it can be applied to any display grid size, and for any target flash frequency. This leads to P300 sequences which are shown here to perform significantly better than the state of the art, in simulations and online tests

Practical Brain Computer Interfacing

A brain-computer interface (BCI) is a communication system that enables users to voluntary send messages or commands without movement. The classical goal of BCI research is to support communication and control for users with impaired communication due to illness or injury. Typical BCI applications are the operation of computer cursors, spelling programs or external devices, such as wheelchairs, robots and neural prostheses. The user sends modulated information to the BCI by engaging in mental tasks that produce distinct brain patterns. The BCI acquires signals from the user's brain and translates them into suitable communication. This dissertation aims to develop faster and more reliable non-invasive BCI communication based on the study of users learning process and their interaction with the BCI transducer. To date, BCI research has focused on the development of advanced pattern recognition and classification algorithms to improve accuracy and reliability of the classified patterns. However, even with optimal detection methods, successful BCI operation depends on the degree to which the users can voluntary modulate their brain signals. Therefore, learning to operate a BCI requires repeated practice with feedback that engages learning mechanisms in the brain. In this work, several aspects including signal processing techniques, feedback methods, experimental and training protocols, demographics, and applications were explored and investigated. Research was focused on two BCI paradigms, steady-state visual evoked potentials (SSVEP) and event-related (de-)synchronization (ERD/ERS). Signal processing algorithms for the detection of both brain patterns were applied and evaluated. A general application interface for BCI feedback tasks was developed to evaluate the practicability, reliability and acceptance of new feedback methods. The role of feedback and training was fully investigated on studies conducted with healthy subjects. The influence of demographics on BCIs was explored in two field studies with a large number of subjects. Results were supported through advanced statistical analysis. Furthermore, the BCI control was evaluated in a spelling application and a service robotic application. This dissertation demonstrates that BCIs can provide effective communication for most subjects. Presented results showed that improvements in the BCI transducer, training protocols, and feedback methods constituted the basis to achieve faster and more reliable BCI communication. Nevertheless, expert assistance is necessary for both initial configuration and daily operation, which reduces the practicability of BCIs for people who really need them