Towards Development of a 3-State Self-Paced Brain-Computer Interface

Most existing brain-computer interfaces (BCIs) detect specific mental activity in a so-called synchronous paradigm. Unlike synchronous systems which are operational at specific system-defined periods, self-paced (asynchronous) interfaces have the advantage of being operational at all times. The low-frequency asynchronous switch design (LF-ASD) is a 2-state self-paced BCI that detects the presence of a specific finger movement in the ongoing EEG. Recent evaluations of the 2-state LF-ASD show an average true positive rate of 41% at the fixed false positive rate of 1%. This paper proposes two designs for a 3-state self-paced BCI that is capable of handling idle brain state. The two proposed designs aim at detecting right- and left-hand extensions from the ongoing EEG. They are formed of two consecutive detectors. The first detects the presence of a right- or a left-hand movement and the second classifies the detected movement as a right or a left one. In an offline analysis of the EEG data collected from four able-bodied individuals, the 3-state brain-computer interface shows a comparable performance with a 2-state system and significant performance improvement if used as a 2-state BCI, that is, in detecting the presence of a right- or a left-hand movement (regardless of the type of movement). It has an average true positive rate of 37.5% and 42.8% (at false positives rate of 1%) in detecting right- and left-hand extensions, respectively, in the context of a 3-state self-paced BCI and average detection rate of 58.1% (at false positive rate of 1%) in the context of a 2-state self-paced BCI

A novel hybrid method for vocal fold pathology diagnosis based on russian language

In this paper, first, an initial feature vector for vocal fold pathology diagnosis is proposed. Then, for optimizing the initial feature vector, a genetic algorithm is proposed. Some experiments are carried out for evaluating and comparing the classification accuracies which are obtained by the use of the different classifiers (ensemble of decision tree, discriminant analysis and K-nearest neighbours) and the different feature vectors (the initial and the optimized ones). Finally, a hybrid of the ensemble of decision tree and the genetic algorithm is proposed for vocal fold pathology diagnosis based on Russian Language. The experimental results show a better performance (the higher classification accuracy and the lower response time) of the proposed method in comparison with the others. While the usage of pure decision tree leads to the classification accuracy of 85.4% for vocal fold pathology diagnosis based on Russian language, the proposed method leads to the 8.5% improvement (the accuracy of 93.9%)

Brain-machine interface using electrocorticography in humans

Paralysis has a severe impact on a patient’s quality of life and entails a high emotional burden and life-long social and financial costs. More than 5 million people in the USA suffer from some form of paralysis and about 50% of the people older than 65 experience difficulties or inabilities with movement. Restoring movement and communication for patients with neurological and motor disorders, stroke and spinal cord injuries remains a challenging clinical problem without an adequate solution. A brain-machine interface (BMI) allows subjects to control a device, such as a computer cursor or an artificial hand, exclusively by their brain activity. BMIs can be used to control communication and prosthetic devices, thereby restoring the communication and movement capabilities of the paralyzed patients. So far, most powerful BMIs have been realized by extracting movement parameters from the activity of single neurons. To record such activity, electrodes have to penetrate the brain tissue, thereby generating risk of brain injury. In addition, recording instability, due to small movements of the electrodes within the brain and the neuronal tissue response to the electrode implant, is also an issue. In this thesis, I investigate whether electrocorticography (ECoG), an alternative recording technique, can be used to achieve BMIs with similar accuracy. First, I demonstrate a BMI based on the approach of extracting movement parameters from ECoG signals. Such ECoG based BMI can further be improved using supervised adaptive algorithms. To implement such algorithms, it is necessary to continuously receive feedback from the subject whether the BMI-decoded trajectory was correct or incorrect. I show that, by using the same ECoG recordings, neuronal responses to trajectory errors can be recorded, detected and differentiated from other types of errors. Finally, I devise a method that could be used to improve the detection of error related neuronal responses

ECoG correlates of visuomotor transformation, neural plasticity, and application to a force-based brain computer interface

Electrocorticography: ECoG) has gained increased notoriety over the past decade as a possible recording modality for Brain-Computer Interface: BCI) applications that offers a balance of minimal invasiveness to the patient in addition to robust spectral information over time. More recently, the scale of ECoG devices has begun to shrink to the order of micrometer diameter contacts and millimeter spacings with the intent of extracting more independent signals for BCI control within less cortical real-estate. However, most control signals to date, whether within the field of ECoG or any of the more seasoned recording techniques, have translated their control signals to kinematic control parameters: i.e. position or velocity of an object) which may not be practical for certain BCI applications such as functional neuromuscular stimulation: FNS). Thus, the purpose of this dissertation was to present a novel application of ECoG signals to a force-based control algorithm and address its feasibility for such a BCI system. Micro-ECoG arrays constructed from thin-film polyimide were implanted epidurally over areas spanning premotor, primary motor, and parietal cortical areas of two monkeys: three hemispheres, three arrays). Monkeys first learned to perform a classic center-out task using a brain signal-to-velocity mapping for control of a computer cursor. The BCI algorithm utilized day-to-day adaptation of the decoding model to match the task intention of the monkeys with no need for pre-screeening of movement-related ECoG signals. Using this strategy, subjects showed notable 2-D task profiency and increased task-related modulation of ECoG features within five training sessions. After fixing the last model trained for velocity control of the cursor, the monkeys then utilized this decoding model to control the acceleration of the cursor in the same center-out task. Cursor movement profiles under this mapping paralleled those demonstrated using velocity control, and neural control signal profiles revealed the monkeys actively accelerated and decelerated the cursor within a limited time window: 1-1.5 seconds). The fixed BCI decoding model was recast once again to control the force on a virtual cursor in a novel mass-grab task. This task required targets not only to reach to peripheral targets but also account for an additional virtual mass as they grabbed each target and moved it to a second target location in the presence of the external force of gravity. Examination of the ensemble control signals showed neural adaptation to variations in the perceived mass of the target as well as the presence or absence of gravity. Finally, short rest periods were interleaved within blocks of each task type to elucidate differences between active BCI intention and rest. Using a post-hoc state-decoder model, periods of active BCI task control could be distinguished from periods of rest with a very high degree of accuracy: ~99%). Taken together, the results from these experiments present a first step toward the design of a dynamics-based BCI system suitable for FNS applications as well as a framework for implementation of an asyncrhonous ECoG BCI

A quality metric to improve wrapper feature selection in multiclass subject invariant brain computer interfaces

Title from PDF of title page, viewed on June 5, 2012Dissertation advisor: Reza DerakhshaniVitaIncludes bibliographical references (p. 116-129)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2012Brain computer interface systems based on electroencephalograph (EEG) signals have limitations which challenge their application as a practical device for general use. The signal features generated by the brain states we wish to detect possess a high degree of inter-subject and intra-subject variation. Additionally, these features usually exhibit a low variation across each of the target states. Collection of EEG signals using low resolution, non-invasive scalp electrodes further degrades the spatial resolution of these signals. The majority of brain computer interface systems to date require extensive training prior to use by each individual user. The discovery of subject invariant features could reduce or even eliminate individual training requirements. To obtain suitable subject invariant features requires search through a high dimension feature space consisting of combinations of spatial, spectral and temporal features. Poorly separable features can prevent the search from converging to a usable solution as a result of degenerate classifiers. In such instances the system must detect and compensate for degenerate classifier behavior. This dissertation presents a method to accomplish this search using a wrapper architecture comprised of a sequential forward floating search algorithm coupled with a support vector machine classifier. This is successfully achieved by the introduction of a scalar Quality (Q)-factor metric, calculated from the ratio of sensitivity to specificity of the confusion matrix. This method is successfully applied to a multiclass subject independent BCI using 10 untrained subjects performing 4 motor tasks.Introduction to brain computer interface systems -- Historical perspective and state of the art -- Experimental design -- Degeneracy in support vector machines -- Discussion of research -- Results -- Conclusion -- Appendix A. Information transfer rate -- Appendix B. Additional surface plots for individual tasks and subject

Towards development of a 3-state self-paced brain computer interface system

Brain computer interface (BCI) systems aim at helping individuals with motor disabilities by providing them the ability to control devices such as wheelchairs and computers, using their brain activity only. The majority of BCI research to-date has focused on developing technology for "synchronous" BCIs. These systems allow the user to affect control during specified time periods only. Self-paced (asynchronous) BCIs on the other hand, are designed to respond whenever the user intends to control the system, otherwise they remain in the so called "inactive output state". This dissertation pursues two main objectives: (1) improving the performance of the existing 2-state self-paced BCI system developed at the Neil Squire Society, Vancouver, Canada (initial evaluations of this system on eight subjects showed mean true positive (TP) rates of 51.3% and 27.5% at false positive (FP) rates of 2% and 1%, respectively.) and (2) designing the first 3-state self-paced BCI. At first, a comprehensive survey of signal processing algorithms in BCI systems is conducted. This survey is the first comprehensive review that covers more than 300 BCI published papers and introduces a taxonomy for signal processing in BCI systems. To achieve the first objective, four separate studies related to the feature extraction and feature classification blocks of the 2-state self-paced BCI are conducted. These studies increase the mean TP rate of the existing system to 73.5% and 47.3% at the FP rates of 2% and 1%, respectively. In these studies, the users were not allowed to control the BCI in 15-34% of the time due to the presence of eye blinks. Thus, another study is also conducted to evaluate the system when the users were allowed to control the output even during eye blinks. Results show slight decrease in TP rates (mean TP rates of 68.0% and 40.6% at the FP rates of 2% and 1%, respectively) with the advantage of providing full control of the system. To achieve the second objective, two new set of movements (right and left hand extension movements) which have not been previously used in the context of BCI systems are used to control the new 3-state self-paced BCI. Results on four able-bodied subjects show significant improvements in detecting the presence of a movement when the system is used in the context of a 2-state self-paced BCI. The mean TP rate is 73.4% at the FP rate of 1%. Initial evaluations of the proposed 3-state self-paced BCI show promise with mean right and left true positive rates of 42.2% and 51.9% at a false positive rate of 1%.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat

Contributions to physiological computing by means of automatic learning.

169 p.El trabajo presentado en esta tesis se enmarca dentro de dos áreas dentro de la computación fisiológica, que a su vez forma parte de las ciencias de la computación. La primera área trabajada corresponde a la de la detección de fenómenos psicológicos y estados mentales mediante la monitorización de las variables fisiológicas de las personas. La segunda área que se estudia en esta tesis forma parte del estudio de formas alternativas de interacción: los interfaces cerebro-computador.La primera contribución mejora un sistema de lógica difusa que, mediante la monitorización de las señales fisiológicas, es capaz de dar una estimación continuada en el tiempo del nivel del estrés mental. La segunda contribución continua con esta línea y estudia la detección de las respuestas fisiológicas del fenómeno opuesto al estrés: la relajación. En esta contribución se presentan características innovadoras que facilitan dicha detección y la pone en práctica con métodos de aprendizaje automático.Finalmente, la tercera contribución estudia diferentes técnicas de aprendizaje para distinguir entre cuatro clases de movimiento más una quinta clase de no intencionalidad de movimiento en un problema de BCI