Impaired motor function following neurological injury may be overcome through therapies that induce neuroplastic changes in the brain. Therapeutic methods include repetitive exercises that promote use-dependent plasticity (UDP), the benefit of which may be increased by first administering peripheral nerve stimulation (PNS) to activate afferent fibers, resulting in increased cortical excitability. We speculate that PNS delivered only in response to attempted movement would induce timing-dependent plasticity (TDP), a mechanism essential to normal motor learning. Here we develop a brain-machine interface (BMI) to detect movement intent and effort in healthy volunteers (n=5) from their electroencephalogram (EEG). This could be used in the future to promote TDP by triggering PNS in response to a patient’s level of effort in a motor task. Linear classifiers were used to predict state (rest, sham, right, left) based on EEG variables in a handgrip task and to determine between three levels of force applied. Mean classification accuracy with out-of-sample data was 54% (23-73%) for tasks and 44% (21-65%) for force. There was a slight but significant correlation (p<0.001) between sample entropy and force exerted. The results indicate the feasibility of applying PNS in response to motor intent detected from the brain.

The goal of the study was to use electrical signals from primary motor cortex to generate accurate predictions of the movement onset time of performed movements, for potential use in asynchronous brain-machine interface (BMI) systems. Four subjects, two with electroencephalogram and two with electrocorticogram electrodes, performed various movements while activity from their primary motor cortices was recorded. An analysis program used several criteria (change point, fractal dimension, spectral entropy, sum of differences, bandpower, bandpower integral, phase, and variance), derived from the neural recordings, to generate predictions of movement onset time, which it compared to electromyogram activity onset time, determining prediction accuracy by receiver operating characteristic curve areas. All criteria, excepting phase and change-point analysis, generated accurate predictions in some cases.

Brain-machine interfaces (BMIs) hold promise for restoring motor functions in severely paralyzed individuals. Invasive BMIs are capable of recording signals from individual neurons and typically provide the highest signal-to-noise ratio. Despite many efforts in the scientific community, BMI technology is still not reliable enough for widespread clinical application. The most prominent challenges include biocompatibility, stability, longevity, and lack of good models for informed signal processing and BMI comparison. To address the problem of low signal quality of chronic probes, in the first part of the thesis one such design, the Neurotrophic Electrode, was modified by increasing its channel capacity to form a Neurotrophic Array (NA). Specifically, single wires were replaced with stereotrodes and the total number of recording wires was increased. This new array design was tested in a rhesus macaque performing a delayed saccade task. The NA recorded little single unit spiking activity, and its local field potentials (LFPs) correlated with presented visual stimuli and saccade locations better than did extracted spikes. The second part of the thesis compares the NA to the Utah Array (UA), the only other micro-array approved for chronic implantation in a human brain. The UA recorded significantly more spiking units, which had larger amplitudes than NA spikes. This was likely due to differences in the array geometry and construction. LFPs on the NA electrodes were more correlated with each other than those on the UA. These correlations negatively impacted the NA's information capacity when considering more than one recording site. The final part of this dissertation applies information theory to develop objective measures of BMI performance. Currently, decoder information transfer rate (ITR) is the most popular BMI information performance metric. However, it is limited by the selected decoding algorithm and does not represent the full task information embedded in the recorded neural signal. A review of existing methods to estimate ITR is presented, and these methods are interpreted within a BMI context. A novel Gaussian mixture Monte Carlo method is developed to produce good ITR estimates with a low number of trials and high number of dimensions, as is typical for BMI applications.

The last decade has seen a surge in the development of Brain-Machine Interfaces (BMI) as assistive neural devices for paralysis patients. Current BMI research typically involves a subject performing movements by controlling a robotic prosthesis. The neural signal that we consider for analysis is the Local Field Potential (LFP). The LFP is a low frequency neural signal recorded from intra-cortical electrodes, and has been recognized as one containing movement information. This thesis investigates hand-movement prediction using LFP data as input. In Chapter 1, we give an overview of Brain Machine Interfaces. In Chapter 2, we review the necessary concepts in time series analysis and pattern recognition. In the final chapter, we discuss classification accuracies when considering Summed power and Coherence as feature vectors.

Essay I explores brain machine interface (BMI) technologies. These make direct communication between the brain and a machine possible by means of electrical stimuli. This essay reviews the existing and emerging technologies in this field and offers a systematic inquiry into the relevant ethical problems that are likely to emerge in the following decades. Essay II, co-written with professor Sven-Ove Hansson, presents a novel procedure to engage the public in ethical deliberations on the potential impacts of brain machine interface technology. We call this procedure a Convergence seminar, a form of scenario-based group discussion that is founded on the idea of hypothetical retrospection. The theoretical background of this procedure and the results of the five seminars are presented here. Essay III discusses moral enhancement, an instance of human enhancement that alters a person’s dispositions, emotions or behavior in order to make that person more moral. Moral enhancement could be carried out in three different ways. The first strategy is behavioral enhancement. The second strategy, favored by prominent defenders of moral enhancement, is emotional enhancement. The third strategy is the enhancement of moral dispositions, such as empathy and inequity aversion. I argue that we ought to implement a combination of the second and third strategies. / <p>QC 20121206</p>

Brain-machine interfaces (BMIs) are the only currently viable means of communication for many individuals suffering from locked-in syndrome (LIS) – profound paralysis that results in severely limited or total loss of voluntary motor control. By inferring user intent from task-modulated neurological signals and then translating those intentions into actions, BMIs can enable LIS patients increased autonomy. Significant effort has been devoted to developing BMIs over the last three decades, but only recently have the combined advances in hardware, software, and methodology provided a setting to realize the translation of this research from the lab into practical, real-world applications. Non-invasive methods, such as those based on the electroencephalogram (EEG), offer the only feasible solution for practical use at the moment, but suffer from limited communication rates and susceptibility to environmental noise. Maximization of the efficacy of each decoded intention, therefore, is critical. This thesis addresses the challenge of implementing a BMI intended for practical use with a focus on an autonomous assistive robot application. First an adaptive EEG- based BMI strategy is developed that relies upon code-modulated visual evoked potentials (c-VEPs) to infer user intent. As voluntary gaze control is typically not available to LIS patients, c-VEP decoding methods under both gaze-dependent and gaze- independent scenarios are explored. Adaptive decoding strategies in both offline and online task conditions are evaluated, and a novel approach to assess ongoing online BMI performance is introduced. Next, an adaptive neural network-based system for assistive robot control is presented that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. Exploratory learning, or “learning by doing,” is an unsupervised method in which the robot is able to build an internal model for motor planning and coordination based on real-time sensory inputs received during exploration. Finally, a software platform intended for practical BMI application use is developed and evaluated. Using online c-VEP methods, users control a simple 2D cursor control game, a basic augmentative and alternative communication tool, and an assistive robot, both manually and via high-level goal-oriented commands.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第19126号 / 情博第572号 / 新制||情||100(附属図書館) / 32077 / 京都大学大学院情報学研究科システム科学専攻 / (主査)教授 石井 信, 教授 田中 利幸, 教授 加納 学 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Objective An electroencephalographic brain–computer interface (BCI) can provide a non-muscular means of communication for people with amyotrophic lateral sclerosis (ALS) or other neuromuscular disorders. We present a novel P300-based BCI stimulus presentation – the checkerboard paradigm (CBP). CBP performance is compared to that of the standard row/column paradigm (RCP) introduced by Farwell and Donchin (1988). Methods Using an 8 × 9 matrix of alphanumeric characters and keyboard commands, 18 participants used the CBP and RCP in counter-balanced fashion. With approximately 9–12 min of calibration data, we used a stepwise linear discriminant analysis for online classification of subsequent data. Results Mean online accuracy was significantly higher for the CBP, 92%, than for the RCP, 77%. Correcting for extra selections due to errors, mean bit rate was also significantly higher for the CBP, 23 bits/min, than for the RCP, 17 bits/min. Moreover, the two paradigms produced significantly different waveforms. Initial tests with three advanced ALS participants produced similar results. Furthermore, these individuals preferred the CBP to the RCP. Conclusions These results suggest that the CBP is markedly superior to the RCP in performance and user acceptability. Significance The CBP has the potential to provide a substantially more effective BCI than the RCP. This is especially important for people with severe neuromuscular disabilities.

Objective: An electroencephalographic brain-computer interface (BCI) can provide a non-muscular means of communication for people with amyotrophic lateral sclerosis (ALS) or other neuromuscular disorders. We present a novel P300-based BCI stimulus presentation - the checkerboard paradigm (CBP). CBP performance is compared to that of the standard row/column paradigm (RCP) introduced by Farwell and Donchin (1988). Methods: Using an 8 × 9 matrix of alphanumeric characters and keyboard commands, 18 participants used the CBP and RCP in counter-balanced fashion. With approximately 9-12 min of calibration data, we used a stepwise linear discriminant analysis for online classification of subsequent data. Results: Mean online accuracy was significantly higher for the CBP, 92%, than for the RCP, 77%. Correcting for extra selections due to errors, mean bit rate was also significantly higher for the CBP, 23 bits/min, than for the RCP, 17 bits/min. Moreover, the two paradigms produced significantly different waveforms. Initial tests with three advanced ALS participants produced similar results. Furthermore, these individuals preferred the CBP to the RCP. Conclusions: These results suggest that the CBP is markedly superior to the RCP in performance and user acceptability. Significance: The CBP has the potential to provide a substantially more effective BCI than the RCP. This is especially important for people with severe neuromuscular disabilities.

Objective: Brain-computer interface (BCI) technology can provide severely disabled people with non-muscular communication. For those most severely disabled, limitations in eye mobility or visual acuity may necessitate auditory BCI systems. The present study investigates the efficacy of the use of six environmental sounds to operate a 6 × 6 P300 Speller. Methods: A two-group design was used to ascertain whether participants benefited from visual cues early in training. Group A (N = 5) received only auditory stimuli during all 11 sessions, whereas Group AV (N = 5) received simultaneous auditory and visual stimuli in initial sessions after which the visual stimuli were systematically removed. Stepwise linear discriminant analysis determined the matrix item that elicited the largest P300 response and thereby identified the desired choice. Results: Online results and offline analyses showed that the two groups achieved equivalent accuracy. In the last session, eight of 10 participants achieved 50% or more, and four of these achieved 75% or more, online accuracy (2.8% accuracy expected by chance). Mean bit rates averaged about 2 bits/min, and maximum bit rates reached 5.6 bits/min. Conclusions: This study indicates that an auditory P300 BCI is feasible, that reasonable classification accuracy and rate of communication are achievable, and that the paradigm should be further evaluated with a group of severely disabled participants who have limited visual mobility. Significance: With further development, this auditory P300 BCI could be of substantial value to severely disabled people who cannot use a visual BCI.