Browsing by Subject "Brain-computer interface"

Brain-computer interfaces (BCI) have the potential to restore communication or control abilities in individuals with severe neuromuscular limitations, such as those with amyotrophic lateral sclerosis (ALS). The role of a BCI is to extract and decode relevant information that conveys a user's intent directly from brain electro-physiological signals and translate this information into executable commands to control external devices. However, the BCI decision-making process is error-prone due to noisy electro-physiological data, representing the classic problem of efficiently transmitting and receiving information via a noisy communication channel.

This research focuses on P300-based BCIs which rely predominantly on event-related potentials (ERP) that are elicited as a function of a user's uncertainty regarding stimulus events, in either an acoustic or a visual oddball recognition task. The P300-based BCI system enables users to communicate messages from a set of choices by selecting a target character or icon that conveys a desired intent or action. P300-based BCIs have been widely researched as a communication alternative, especially in individuals with ALS who represent a target BCI user population. For the P300-based BCI, repeated data measurements are required to enhance the low signal-to-noise ratio of the elicited ERPs embedded in electroencephalography (EEG) data, in order to improve the accuracy of the target character estimation process. As a result, BCIs have relatively slower speeds when compared to other commercial assistive communication devices, and this limits BCI adoption by their target user population. The goal of this research is to develop algorithms that take into account the physical limitations of the target BCI population to improve the efficiency of ERP-based spellers for real-world communication.

In this work, it is hypothesised that building adaptive capabilities into the BCI framework can potentially give the BCI system the flexibility to improve performance by adjusting system parameters in response to changing user inputs. The research in this work addresses three potential areas for improvement within the P300 speller framework: information optimisation, target character estimation and error correction. The visual interface and its operation control the method by which the ERPs are elicited through the presentation of stimulus events. The parameters of the stimulus presentation paradigm can be modified to modulate and enhance the elicited ERPs. A new stimulus presentation paradigm is developed in order to maximise the information content that is presented to the user by tuning stimulus paradigm parameters to positively affect performance. Internally, the BCI system determines the amount of data to collect and the method by which these data are processed to estimate the user's target character. Algorithms that exploit language information are developed to enhance the target character estimation process and to correct erroneous BCI selections. In addition, a new model-based method to predict BCI performance is developed, an approach which is independent of stimulus presentation paradigm and accounts for dynamic data collection. The studies presented in this work provide evidence that the proposed methods for incorporating adaptive strategies in the three areas have the potential to significantly improve BCI communication rates, and the proposed method for predicting BCI performance provides a reliable means to pre-assess BCI performance without extensive online testing.

Brain-computer interfaces (BCIs) have the potential to restore or replace lost neural output after injury or disease. BCIs operate by processing and interpreting brain signals such as (non-invasive) electroencephalography (EEG) data to decode user intent into commands to control external devices. An important consideration in deploying BCIs is that brain activity is constantly evolving, making long-term accurate interpretation of brain activity a challenge.

This work focuses on the P300 speller, a BCI that can be used to restore communication abilities for individuals whose motor abilities have been severely compromised, such as people with amyotrophic lateral sclerosis (ALS). The P300 speller enables users to select their target character from a set of choices using their brain activity. In order to spell a character, the BCI presents a stream of stimuli to the user, anticipating that event-related potentials (ERPs) will be elicited within EEG data in response to the presentation of a rare stimulus, specifically one containing the target character. The characteristics of ERPs will change over time according to factors such as a user’s level of fatigue. The P300 speller uses a machine learning classifier to interpret the user’s brain signals; this classifier is conventionally trained in a supervised manner with EEG data during a calibration phase, with its parameters remaining static as it is applied to real-time EEG data during online operation. As this BCI is used to meet long-term communication needs, it is important to maintain the performance of the P300 speller over longitudinal use. Since the statistical properties of brain signals change over time, the current and most recently available brain signals are most suitable for understanding a user’s current cognitive state. This work focuses on developing methods to enhance the use of real-time data during online BCI operation in order to improve BCI performance.

In this work, it is hypothesized that further incorporating recent online EEG data and BCI decision-making into the P300 speller framework can enhance BCI performance by providing more up-to-date information regarding the user’s intent. This research addresses methods of improving three areas of the P300 speller framework: stimulus selection, classifier learning, and representation learning. During a spelling trial, i.e., the series of stimuli used to select a user’s target character, the presentation of P300 speller stimuli directly influences how ERPs are elicited. The stimulus selection process can be optimized to improve the information content in presented stimuli based on the EEG data and stimulus presentation history from the trial. A new stimulus presentation paradigm is developed with this optimization goal, and which also focuses on designing stimulus characteristics to mitigate psychophysical effects that can negatively influence ERP elicitation. Next, as changing brain activity makes the most recent brain signals more appropriate for interpreting a user’s intent than older brain signals from offline calibration, the BCI classifier learning strategy can be modified from the conventional static approach to an adaptive approach that utilizes real-time EEG data. A semi-supervised learning strategy that relies on EEG data and ground truth labels from BCI calibration, as well as online EEG data and classifier-predicted labels, is explored. This strategy incorporates a language model in order to improve the quality of the classifier-predicted labels used for continual online learning. Finally, attention-based models afford a method to perform contextual representation learning with EEG data. The inputs to an attention-based model can be enhanced for the BCI use case with the goal of learning more robust data representations. As the stimulus presentation sequence is directly connected to the ERP elicitation patterns in a trial, providing this BCI decision-making history can improve the data context available to the model. The studies presented within this work provide evidence that these proposed methods for further integrating real-time EEG data and BCI decision-making history into the spelling framework have the potential to significantly improve BCI communication rates, including over long-term use of the BCI.

Brain-machine interfaces (BMI) are systems which connect brains directly to machines or computers for communication. BMI-controlled prosthetic devices use algorithms to decode neuronal recordings into movement commands. These algorithms operate using models of how recorded neuronal signals relate to desired movements, called models of tuning. Models of tuning have typically been linear in prior work, due to the simplicity and speed of the algorithms used with them. Neuronal tuning has been shown to slowly change over time, but most prior work do not adapt tuning models to these changes. Furthermore, extracellular electrical recordings of neurons' action potentials slowly change over time, impairing the preprocessing step of spike-sorting, during which the neurons responsible for recorded action potentials are identified.

This dissertation presents a non-linear adaptive Bayesian filter and an adaptive spike-sorting method for BMI decoding. The adaptive filter consists of the n-th order unscented Kalman filter and Bayesian regression self-training updates. The unscented Kalman filter estimates desired prosthetic movements using a non-linear model of tuning as its observation model. The model is quadratic with terms for position, velocity, distance from center of workspace, and velocity magnitude. The tuning model relates neuronal activity to movements at multiple time offsets simultaneously, and the movement model of the filter is an order n autoregressive model.

To adapt the tuning model parameters to changes in the brain, Bayesian regression self-training updates are performed periodically. Tuning model parameters are stored as probability distributions instead of point estimates. Bayesian regression uses the previous model parameters as priors and calculates the posteriors of the regression between filter outputs, which are assumed to be the desired movements, and neuronal recordings. Before each update, filter outputs are smoothed using a Kalman smoother, and tuning model parameters are passed through a transition model describing how parameters change over time. Two variants of Bayesian regression are presented: one uses a joint distribution for the model parameters which allows analytical inference, and the other uses a more flexible factorized distribution that requires approximate inference using variational Bayes.

To adapt spike-sorting parameters to changes in spike waveforms, variational Bayesian Gaussian mixture clustering updates are used to update the waveform clustering used to calculate these parameters. This Bayesian extension of expectation-maximization clustering uses the previous clustering parameters as priors and computes the new parameters as posteriors. The use of priors allows tracking of clustering parameters over time and facilitates fast convergence.

To evaluate the proposed methods, experiments were performed with 3 Rhesus monkeys implanted with micro-wire electrode arrays in arm-related areas of the cortex. Off-line reconstructions and on-line, closed-loop experiments with brain-control show that the n-th order unscented Kalman filter is more accurate than previous linear methods. Closed-loop experiments over 29 days show that Bayesian regression self-training helps maintain control accuracy. Experiments on synthetic data show that Bayesian regression self-training can be applied to other tracking problems with changing observation models. Bayesian clustering updates on synthetic and neuronal data demonstrate tracking of cluster and waveform changes. These results indicate the proposed methods improve the accuracy and robustness of BMIs for prosthetic devices, bringing BMI-controlled prosthetics closer to clinical use.