Transfer Learning for Motor Imagery based Brain Computer Interfaces

Current electroencephalogram (EEG) based brain-computer interface (BCI) systems have limited real-world practicality due to a number of issues, including the long calibration period required before each use. This thesis focuses on reducing the time required to calibrate the BCI system without sacrificing classification accuracy. To address this issue, previously collected EEG data could be potentially mined and reused in calibrating the BCI model for a new user/session. However, this is not a trivial task due to two key challenges. First, there are considerable non-stationarities between the current and previously collected EEG signals. Secondly, due to between-session variations, not all the previously collected EEG signals are helpful in training the new BCI model. Initially, the thesis explored the application of distribution alignment techniques to reduce the effects of EEG non-stationarity. A novel multiclass data space alignment (MDSA) algorithm was proposed and evaluated. Our results showed that the proposed MDSA alignment algorithm successfully improved the classification accuracy and reduced the effects of non-stationarity. The thesis then addressed the second challenge by developing a new framework. This framework utilised a new algorithm that identifies whether or not the new session would benefit from transfer learning. If so, a novel similarity measurement, called the Jensen-Shannon Ratio (JSR), was proposed to select one of the past session for training the BCI model. The proposed framework outperformed state-of-the-art algorithms when there were as few as five labelled trials per class available from the new session. Despite success to some extent the proposed framework was limited to a binary selection between only one of the past sessions and current data for training the BCI model. Finally, the thesis utilised the findings of the previous research in order to address both challenges. A novel transfer learning framework was proposed for long-term BCI users. The proposed framework utilised regularisation, alignment and weighting to train a BCI which outperformed state-of-the-art algorithms even when only two trials per class from the new session were available

Towards a Spatial Ability Training to Improve Mental Imagery based Brain-Computer Interface (MI-BCI) Performance: a Pilot Study

International audience—Although Mental Imagery based Brain-Computer Interfaces (MI-BCIs) seem to be very promising for many applications, they are still rarely used outside laboratories. This is partly due to suboptimal training protocols, which provide little help to users learning how to control the system. Indeed, they do not take into account recommendations from instructional design. However, it has been shown that MI-BCI performances are significantly correlated to certain aspects of the users' cognitive profile, such as their Spatial Abilities (SA). Thus, it remains to be elucidated whether training the SA of BCI users would also improve their BCI control performance. Therefore, we proposed and validated an SA training that aimed at being included in an MI-BCI training protocol. Our pre-studies indeed confirmed that such a training does increase people's SA abilities. We then conducted a pilot study with 3 participants, one with a standard MI-BCI training protocol, one with the proposed SA training integrated into a standard MI-BCI training, and another control integrating another training, here verbal comprehension tasks, into a standard MI-BCI training. While such a small population cannot lead to any strong result, our first results show that SA training can indeed be integrated into MI-BCI training and is thus worth being further investigated for BCI user training

Training Users' Spatial Abilities to Improve Brain-Computer Interface Performance: A Theoretical Approach

National audience—Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) allow their users to send commands to a computer using their brain activity alone (typically measured by ElectroEn-cephaloGraphy-EEG), which is processed while they perform specific mental tasks. While very promising MI-BCIs remain barely used outside laboratories because of the difficulty encountered by users to control them. Indeed, although some users obtain good control performances after training, a substantial proportion remains unable to reliably control an MI-BCI. This huge variability in user performance led the community to look for predictors of MI-BCI control ability. Mainly, neurophysiolog-ical and psychological predictors of MI-BCI performance have been proposed. In this paper, a newly-depicted lever to increase MI-BCI performance is introduced: namely a spatial ability training. The aims of this paper are to clarify the relationship between spatial abilities and mental imagery tasks used in MI-BCI paradigms, and to provide suggestions to include a spatial ability training in MI-BCI training protocols

TOWARDS PREDICTION AND IMPROVEMENT OF EEG-BASED MI-BCI PERFORMANCE.

Ph.DDOCTOR OF PHILOSOPH

Subject-independent EEG classification based on a hybrid neural network

A brain-computer interface (BCI) based on the electroencephalograph (EEG) signal is a novel technology that provides a direct pathway between human brain and outside world. For a traditional subject-dependent BCI system, a calibration procedure is required to collect sufficient data to build a subject-specific adaptation model, which can be a huge challenge for stroke patients. In contrast, subject-independent BCI which can shorten or even eliminate the pre-calibration is more time-saving and meets the requirements of new users for quick access to the BCI. In this paper, we design a novel fusion neural network EEG classification framework that uses a specially designed generative adversarial network (GAN), called a filter bank GAN (FBGAN), to acquire high-quality EEG data for augmentation and a proposed discriminative feature network for motor imagery (MI) task recognition. Specifically, multiple sub-bands of MI EEG are first filtered using a filter bank approach, then sparse common spatial pattern (CSP) features are extracted from multiple bands of filtered EEG data, which constrains the GAN to maintain more spatial features of the EEG signal, and finally we design a convolutional recurrent network classification method with discriminative features (CRNN-DF) to recognize MI tasks based on the idea of feature enhancement. The hybrid neural network proposed in this study achieves an average classification accuracy of 72.74 ± 10.44% (mean ± std) in four-class tasks of BCI IV-2a, which is 4.77% higher than the state-of-the-art subject-independent classification method. A promising approach is provided to facilitate the practical application of BCI

Enhanced lower-limb motor imagery by kinesthetic illusion

Brain-computer interface (BCI) based on lower-limb motor imagery (LMI) enables hemiplegic patients to stand and walk independently. However, LMI ability is usually poor for BCI-illiterate (e.g., some stroke patients), limiting BCI performance. This study proposed a novel LMI-BCI paradigm with kinesthetic illusion(KI) induced by vibratory stimulation on Achilles tendon to enhance LMI ability. Sixteen healthy subjects were recruited to carry out two research contents: (1) To verify the feasibility of induced KI by vibrating Achilles tendon and analyze the EEG features produced by KI, research 1 compared the subjective feeling and brain activity of participants during rest task with and without vibratory stimulation (V-rest, rest). (2) Research 2 compared the LMI-BCI performance with and without KI (KI-LMI, no-LMI) to explore whether KI enhances LMI ability. The analysis methods of both experiments included classification accuracy (V-rest vs. rest, no-LMI vs. rest, KI-LMI vs. rest, KI-LMI vs. V-rest), time-domain features, oral questionnaire, statistic analysis and brain functional connectivity analysis. Research 1 verified that induced KI by vibrating Achilles tendon might be feasible, and provided a theoretical basis for applying KI to LMI-BCI paradigm, evidenced by oral questionnaire (Q1) and the independent effect of vibratory stimulation during rest task. The results of research 2 that KI enhanced mesial cortex activation and induced more intensive EEG features, evidenced by ERD power, topographical distribution, oral questionnaire (Q2 and Q3), and brain functional connectivity map. Additionally, the KI increased the offline accuracy of no-LMI/rest task by 6.88 to 82.19% (p < 0.001). The simulated online accuracy was also improved for most subjects (average accuracy for all subjects: 77.23% > 75.31%, and average F1_score for all subjects: 76.4% > 74.3%). The LMI-BCI paradigm of this study provides a novel approach to enhance LMI ability and accelerates the practical applications of the LMI-BCI system

Study of Adaptation Methods Towards Advanced Brain-computer Interfaces

Ph.DDOCTOR OF PHILOSOPH