EEG Signal Processing and Classification for the Novel Tactile-Force Brain-Computer Interface Paradigm

The presented study explores the extent to which tactile-force stimulus delivered to a hand holding a joystick can serve as a platform for a brain computer interface (BCI). The four pressure directions are used to evoke tactile brain potential responses, thus defining a tactile-force brain computer interface (tfBCI). We present brain signal processing and classification procedures leading to successful interfacing results. Experimental results with seven subjects performing online BCI experiments provide a validation of the hand location tfBCI paradigm, while the feasibility of the concept is illuminated through remarkable information-transfer rates.Comment: 6 pages (in conference proceedings original version); 6 figures, submitted to The 9th International Conference on Signal Image Technology & Internet Based Systems, December 2-5, 2013, Kyoto, Japan; to be available at IEEE Xplore; IEEE Copyright 201

Student Teaching and Research Laboratory Focusing on Brain-computer Interface Paradigms - A Creative Environment for Computer Science Students -

This paper presents an applied concept of a brain-computer interface (BCI) student research laboratory (BCI-LAB) at the Life Science Center of TARA, University of Tsukuba, Japan. Several successful case studies of the student projects are reviewed together with the BCI Research Award 2014 winner case. The BCI-LAB design and project-based teaching philosophy is also explained. Future teaching and research directions summarize the review.Comment: 4 pages, 4 figures, accepted for EMBC 2015, IEEE copyrigh

Co-adaptive control strategies in assistive Brain-Machine Interfaces

A large number of people with severe motor disabilities cannot access any of the available control inputs of current assistive products, which typically rely on residual motor functions. These patients are therefore unable to fully benefit from existent assistive technologies, including communication interfaces and assistive robotics. In this context, electroencephalography-based Brain-Machine Interfaces (BMIs) offer a potential non-invasive solution to exploit a non-muscular channel for communication and control of assistive robotic devices, such as a wheelchair, a telepresence robot, or a neuroprosthesis. Still, non-invasive BMIs currently suffer from limitations, such as lack of precision, robustness and comfort, which prevent their practical implementation in assistive technologies. The goal of this PhD research is to produce scientific and technical developments to advance the state of the art of assistive interfaces and service robotics based on BMI paradigms. Two main research paths to the design of effective control strategies were considered in this project. The first one is the design of hybrid systems, based on the combination of the BMI together with gaze control, which is a long-lasting motor function in many paralyzed patients. Such approach allows to increase the degrees of freedom available for the control. The second approach consists in the inclusion of adaptive techniques into the BMI design. This allows to transform robotic tools and devices into active assistants able to co-evolve with the user, and learn new rules of behavior to solve tasks, rather than passively executing external commands. Following these strategies, the contributions of this work can be categorized based on the typology of mental signal exploited for the control. These include: 1) the use of active signals for the development and implementation of hybrid eyetracking and BMI control policies, for both communication and control of robotic systems; 2) the exploitation of passive mental processes to increase the adaptability of an autonomous controller to the user\u2019s intention and psychophysiological state, in a reinforcement learning framework; 3) the integration of brain active and passive control signals, to achieve adaptation within the BMI architecture at the level of feature extraction and classification

A brain-computer interface with vibrotactile biofeedback for haptic information

<p>Abstract</p> <p>Background</p> <p>It has been suggested that Brain-Computer Interfaces (BCI) may one day be suitable for controlling a neuroprosthesis. For closed-loop operation of BCI, a tactile feedback channel that is compatible with neuroprosthetic applications is desired. Operation of an EEG-based BCI using only <it>vibrotactile feedback</it>, a commonly used method to convey haptic senses of contact and pressure, is demonstrated with a high level of accuracy.</p> <p>Methods</p> <p>A Mu-rhythm based BCI using a motor imagery paradigm was used to control the position of a virtual cursor. The cursor position was shown visually as well as transmitted haptically by modulating the intensity of a vibrotactile stimulus to the upper limb. A total of six subjects operated the BCI in a two-stage targeting task, receiving only vibrotactile biofeedback of performance. The location of the vibration was also systematically varied between the left and right arms to investigate location-dependent effects on performance.</p> <p>Results and Conclusion</p> <p>Subjects are able to control the BCI using only vibrotactile feedback with an average accuracy of 56% and as high as 72%. These accuracies are significantly higher than the 15% predicted by random chance if the subject had no voluntary control of their Mu-rhythm. The results of this study demonstrate that vibrotactile feedback is an effective biofeedback modality to operate a BCI using motor imagery. In addition, the study shows that placement of the vibrotactile stimulation on the biceps ipsilateral or contralateral to the motor imagery introduces a significant bias in the BCI accuracy. This bias is consistent with a drop in performance generated by stimulation of the contralateral limb. Users demonstrated the capability to overcome this bias with training.</p

Using novel stimuli and alternative signal processing techniques to enhance BCI paradigms

A Brain-Computer Interface (BCI) is a device that uses the brain activity of a person as an input to select desired outputs on a computer. BCIs that use surface electroencephalogram (EEG) recordings as their input are the least invasive but also suffer from a very low signal-to-noise ratio (SNR) due to the very low amplitude of the person’s brain activity and the presence of many signal artefacts and background noise. This can be compensated for by subjecting the signals to extensive signal processing, and by using stimuli to trigger a large but consistent change in the signal – these changes are called evoked potentials. The method used to stimulate the evoked potential, and introduce an element of conscious selection in order to allow the user’s intent to modify the evoked potential produced, is called the BCI paradigm. However, even with these additions the performance of BCIs used for assistive communication and control is still significantly below that of other assistive solutions, such as keypads or eye-tracking devices. This thesis examines the paradigm and signal processing components of BCIs and puts forward several methods meant to enhance BCIs’ performance and efficiency. Firstly, two novel signal processing methods based on Empirical Mode Decomposition (EMD) were developed and evaluated. EMD is a technique that divides any oscillating signal into groups of frequency harmonics, called Intrinsic Mode Functions (IMFs). Furthermore, by using Takens’ theorem, a single channel of EEG can be converted into a multi-temporal channel signal by transforming the channel into multiple snapshots of its signal content in time using a series of delay vectors. This signal can then be decomposed into IMFs using a multi-channel variation of EMD, called Multi-variate EMD (MEMD), which uses the spatial information from the signal’s neighbouring channels to inform its decomposition. In the case of a multi-temporal channel signal, this allows the temporal dynamics of the signal to be incorporated into the IMFs. This is called Temporal MEMD (T-MEMD). The second signal processing method based on EMD decomposed both the spatial and temporal channels simultaneously, allowing both spatial and temporal dynamics to be incorporated into the resulting IMFs. This is called Spatio-temporal MEMD (ST-MEMD). Both methods were applied to a large pre-recorded Motor Imagery BCI dataset along with EMD and MEMD for comparison. These results were also compared to those from other studies in the literature that had used the same dataset. T-MEMD performed with an average classification accuracy of 70.2%, performing on a par with EMD that had an average classification accuracy of 68.9%. Both ST-MEMD and MEMD outperformed them with ST-MEMD having an average classification accuracy of 73.6%, and MEMD having an average classification accuracy of 75.3%. The methods containing spatial dynamics, i.e. MEMD and ST-MEMD, outperformed those with only temporal dynamics, i.e. EMD and T-MEMD. The two methods with temporal dynamics each performed on a par with the non-temporal method that had the same level of spatial dynamics. This shows that only the presence of spatial dynamics resulted in a performance increase. This was concluded to be because the differences between the classes of motor-imagery are inherently spatial in nature, not temporal. Next a novel BCI paradigm was developed based on the standard Steady-state Somatosensory Evoked Potential (SSSEP) BCI paradigm. This paradigm uses a tactile stimulus applied to the skin at a certain frequency, generating a resonance signal in the brain’s activity. If two stimuli of different frequency are applied, two resonance signals will be present. However, if the user attends one stimulus over the other, its corresponding SSSEP will increase in amplitude. Unfortunately these changes in amplitude can be very minute. To counter this, a stimulus amplitude and frequency of the vibrotactile stimuli. It was hypothesised that if the stimuli generator was constructed that could alter the were of the same frequency, but one’s amplitude was just below the user’s conscious level of perception and the other was above it, the changes in the SSSEP between classes would be the same as those between an SSSEP being generated and neutral EEG, with differences in α activity between the low-amplitude SSSEP and neutral activity due to the differences in the user’s level of concentration from attending the low-amplitude stimulus. The novel SSSEP BCI paradigm performed on a par with the standard paradigm with an average 61.8% classification accuracy over 16 participants, compared to an average 63.3% classification accuracy respectively, indicating that the hypothesis was false. However, the large presence of electro-magnetic interference (EMI) in the EEG recordings may have compromised the data. Many different noise suppression methods were applied to the stimulus device and the data, and whilst the EMI artefacts were reduced in magnitude they were not eliminated completely. Even with the noise the standard SSSEP stimulus paradigm performed on a par with studies that used the same paradigm, indicating that the results may not have been invalidated by the EMI. Overall the thesis shows that motor-imagery signals are inherently spatial in difference, and that the novel methods of T-MEMD and ST-MEMD may yet out-perform the existing methods of EMD and MEMD if applied to signals that are temporal in nature, such as functional Magnetic Resonance Imaging (fMRI). Whilst the novel SSSEP paradigm did not result in an increase in performance, it highlighted the impact of EMI from stimulus equipment on EEG recordings and potentially confirmed that the amplitude of SSEP stimuli is a minor factor in a BCI paradigm

Decoding Mental States after Severe Brain Injury

Some patients with disorders of consciousness retain sensory and cognitive abilities that are not apparent from their outward behaviour. It is crucial to identify and characterise these covert abilities for diagnosis, prognosis, and medical ethics. This thesis uses neuroimaging techniques to investigate cognitive preservation and awareness in patients who are behaviourally non-responsive due to acquired brain injuries. In the first chapter, a large sample of healthy volunteers, including experienced athletes and musicians, imagined actions of varying complexity and familiarity. Motor imagery involving certain complex, familiar actions correlated with a more robust sensorimotor rhythm. In the second chapter, several patients with disorders of consciousness participated in multiple experiments based on neural responses to mental imagery, including one task featuring complex, familiar imagined actions. Although the patients did not generate enhanced sensorimotor rhythms for the complex, familiar motor imagery, the detection of covert cognition was more sensitive owing to the multi-modal nature of the assessment. In the final empirical chapter, a sample of healthy volunteers and a heterogeneous cohort of patients with disorders of consciousness completed a novel oddball task based on tactile stimulation. Critically, this task delineated an attentional hierarchy in the patient sample, and patients with the ability to follow commands were differentiated from those unable to do so by event-related potential evidence of attentional orienting. Due to the heterogeneity of aetiology and pathology in the disorders of consciousness, these patients vary in their suitability for neuroimaging, the preservation of neural structures, and the cognitive resources available to them. Assessments of several perceptual and cognitive abilities supported by spatially-distinct brain regions and indexed by multiple neural signatures are therefore required to accurately characterise a patient’s abilities and probable subjective experience