To date, invasive brain-computer interface (BCI) research has largely focused on replacing lost limb functions using signals from hand/arm areas of motor cortex. However, the oculomotor system may be better suited to BCI applications involving rapid serial selection from spatial targets, such as choosing from a set of possible words displayed on a computer screen in an augmentative and alternative communication application. First, we develop an intracortical oculomotor BCI based on the delayed saccade paradigm and demonstrate its feasibility to decode intended saccadic eye movement direction in primates. Using activity from three frontal cortical areas implicated in oculomotor production – dorsolateral prefrontal cortex, supplementary eye field, and frontal eye field – we could decode intended saccade direction in real time with high accuracy, particularly at contralateral locations. In a number of analyses in the decoding context, we investigated the amount of saccade-related information contained in different implant regions and in different neural measures. A novel neural measure using power in the 80-500 Hz band is proposed as the optimal signal for this BCI purpose. In the second part of this thesis, we characterize the interactions between the neural signals recorded from electrodes in these three implant areas. We employ a number of techniques to quantify the spectrotemporal dynamics in this complex network, and we describe the resulting functional connectivity patterns between the three implant regions in the context of eye-movement production. In addition, we compare and contrast the amount of saccade-related information present in the coupling strengths in the network, on both an electrode-to-electrode scale and an area-to-area scale. Different frequency bands stand out during different epochs of the task, and their information contents are distinct between implant regions. For example, the 13-30 Hz band stands out during the delay epoch, and the 8-12 Hz band is relevant during target and response epochs. This work extends the boundary of BCI research into the oculomotor domain, and invites potential applications by showing its feasibility. Furthermore, it elucidates the complex dynamics of the functional coupling underlying oculomotor production across multiple areas of frontal cortex.

The recent advent of consumer grade Brain-Computer Interfaces (BCI) provides a new revolutionary and accessible way to control computers. BCI translate cognitive electroencephalography (EEG) signals into computer or robotic commands using specially built headsets. Capable of enhancing traditional interfaces that require interaction with a keyboard, mouse or touchscreen, BCI systems present tremendous opportunities to benefit various fields. Movement restricted users can especially benefit from these interfaces. In this thesis, we present a new way to interface a consumer-grade BCI solution to a mobile robot. A Red-Green-Blue-Depth (RGBD) camera is used to enhance the navigation of the robot with cognitive thoughts as commands. We introduce an interface presenting 3 different methods of robot-control: 1) a fully manual mode, where a cognitive signal is interpreted as a command, 2) a control-flow manual mode, reducing the likelihood of false-positive commands and 3) an automatic mode assisted by a remote RGBD camera. We study the application of this work by navigating the mobile robot on a planar surface using the different control methods while measuring the accuracy and usability of the system. Finally, we assess the newly designed interface’s role in the design of future generation of BCI solutions.

A Human-Machine Interface is a device that allows humans to inter- act with and use machines. One such device is a Brain-Computer Interface which allows the user to communicate to a computer system through thought patterns. A commonly used technique, electroencephalography, uses multiple sensors positioned on the subject’s cranium to extract electrical changes as a representation of thought patterns. This report investigates the use of a single EEG sensor as a user-friendly BCI implementation. The primary goal of this report is to determine if specific mental tasks can be reliably detected with such a system. / text

The recent advent of consumer grade Brain-Computer Interfaces (BCI) provides a new revolutionary and accessible way to control computers. BCI translate cognitive electroencephalography (EEG) signals into computer or robotic commands using specially built headsets. Capable of enhancing traditional interfaces that require interaction with a keyboard, mouse or touchscreen, BCI systems present tremendous opportunities to benefit various fields. Movement restricted users can especially benefit from these interfaces. In this thesis, we present a new way to interface a consumer-grade BCI solution to a mobile robot. A Red-Green-Blue-Depth (RGBD) camera is used to enhance the navigation of the robot with cognitive thoughts as commands. We introduce an interface presenting 3 different methods of robot-control: 1) a fully manual mode, where a cognitive signal is interpreted as a command, 2) a control-flow manual mode, reducing the likelihood of false-positive commands and 3) an automatic mode assisted by a remote RGBD camera. We study the application of this work by navigating the mobile robot on a planar surface using the different control methods while measuring the accuracy and usability of the system. Finally, we assess the newly designed interface’s role in the design of future generation of BCI solutions.

Today smartphones are everywhere and they ease the lives of millions of people every day. However there are people who, because of various reasons, are unable to receive the benefits of these devices because they are not able to interact with a smartphone in the intended way; using their hands. In this thesis we investigate an alternative method for interacting with a smartphone; using a commercially available electroencephalography (EEG) headset. EEG is a technique for measuring and recording brain activity, often through the use of sensors placed along the scalp of the user. We developed a prototype of a brain-computer interface (BCI) for use with android and the Emotiv Insight commercial EEG headset. The prototype allows the user to control an on-screen cursor in one dimension within an android application using the Emotiv Insight. We performed three case studies with one participant in each. The participants had no prior experience with EEG headsets or BCIs. We had them train to use the Emotiv Insight with our BCI prototype. After the training was completed they performed a series of tests in order to measure their ability to control an on-screen cursor in one dimension. Finally the participants filled out a questionnaire regarding their subjective experiences of using the Emotiv Insight. These case studies showed the inadequacies of the Emotiv Insight. All three participants had issues with training and using the headset. These issues are reflected in our tests, where 44 out of 45 attempts at moving the cursor to a specific area resulted in a failure. All participants also reported fatigue and headaches during the case studies. We also concluded that the Emotiv Insight provides a poor user experience because of fatigue in longer sessions and the amount of work needed to train the headset.

abstract: When surgical resection becomes necessary to alleviate a patient's epileptiform activity, that patient is monitored by video synchronized with electrocorticography (ECoG) to determine the type and location of seizure focus. This provides a unique opportunity for researchers to gather neurophysiological data with high temporal and spatial resolution; these data are assessed prior to surgical resection to ensure the preservation of the patient's quality of life, e.g. avoid the removal of brain tissue required for speech processing. Currently considered the "gold standard" for the mapping of cortex, electrical cortical stimulation (ECS) involves the systematic activation of pairs of electrodes to localize functionally specific brain regions. This method has distinct limitations, which often includes pain experienced by the patient. Even in the best cases, the technique suffers from subjective assessments on the parts of both patients and physicians, and high inter- and intra-observer variability. Recent advances have been made as researchers have reported the localization of language areas through several signal processing methodologies, all necessitating patient participation in a controlled experiment. The development of a quantification tool to localize speech areas in which a patient is engaged in an unconstrained interpersonal conversation would eliminate the dependence of biased patient and reviewer input, as well as unnecessary discomfort to the patient. Post-hoc ECoG data were gathered from five patients with intractable epilepsy while each was engaged in a conversation with family members or clinicians. After the data were separated into different speech conditions, the power of each was compared to baseline to determine statistically significant activated electrodes. The results of several analytical methods are presented here. The algorithms did not yield language-specific areas exclusively, as broad activation of statistically significant electrodes was apparent across cortical areas. For one patient, 15 adjacent contacts along superior temporal gyrus (STG) and posterior part of the temporal lobe were determined language-significant through a controlled experiment. The task involved a patient lying in bed listening to repeated words, and yielded statistically significant activations that aligned with those of clinical evaluation. The results of this study do not support the hypothesis that unconstrained conversation may be used to localize areas required for receptive and productive speech, yet suggests a simple listening task may be an adequate alternative to direct cortical stimulation. / Dissertation/Thesis / M.S. Bioengineering 2013

BCI (Brain Computer Interface) för användning i spel har börjat dyka upp på konsument-marknaden. Dessa använder ofta EEG för att mäta spelarnas avslappningsnivåer och den spelare som lyckas nå högsta meditativa tillstånd samlar poäng. I detta arbete undersöks huruvida det går att använda BCI för mer direkt kontroll av en specifik spelmekanik i kombination med konventionella spelkontroller. För undersökningen har två versioner av ett spel skapats med avsikten att mäta skillnaden i hjärnaktiviteten hos spelare som bara spelar, jämfört med spelare som aktivt försöker åstadkomma ett specifikt sinnestillstånd för att direkt kontrollera en funktion i spelet. På grund av studiens begränsade omfattning kunde inga definitiva slutsatser dras men det finns indikationer på att MindWave, som var utrustningen som användes i denna studie, inte är lämplig som direkt kontrollmetod tillsammans med andra kontroller. Studien skulle kunna fungera som en pilotstudie för en mer omfattande undersökning inom ämnet.

The recent advent of consumer grade Brain-Computer Interfaces (BCI) provides a new revolutionary and accessible way to control computers. BCI translate cognitive electroencephalography (EEG) signals into computer or robotic commands using specially built headsets. Capable of enhancing traditional interfaces that require interaction with a keyboard, mouse or touchscreen, BCI systems present tremendous opportunities to benefit various fields. Movement restricted users can especially benefit from these interfaces. In this thesis, we present a new way to interface a consumer-grade BCI solution to a mobile robot. A Red-Green-Blue-Depth (RGBD) camera is used to enhance the navigation of the robot with cognitive thoughts as commands. We introduce an interface presenting 3 different methods of robot-control: 1) a fully manual mode, where a cognitive signal is interpreted as a command, 2) a control-flow manual mode, reducing the likelihood of false-positive commands and 3) an automatic mode assisted by a remote RGBD camera. We study the application of this work by navigating the mobile robot on a planar surface using the different control methods while measuring the accuracy and usability of the system. Finally, we assess the newly designed interface’s role in the design of future generation of BCI solutions.

Advances in brain science and computer technology in the past decade have led to exciting developments in brain-computer interface (BCI), thereby making BCI a top research area in applied science. The renaissance of BCI opens new methods of neurorehabilitation for physically disabled people (e.g. paralyzed patients and amputees) and patients with brain injuries (e.g. stroke patients). Recent technological advances such as wireless recording, machine learning analysis, and real-time temporal resolution have increased interest in electroencephalographic (EEG) based BCI approaches. Many BCI studies have focused on decoding EEG signals associated with whole-body kinematics/kinetics, motor imagery, and various senses. Thus, there is a need to understand the various experimental paradigms used in EEG-based BCI systems. Moreover, given that there are many available options, it is essential to choose the most appropriate BCI application to properly manipulate a neuroprosthetic or neurorehabilitation device. The current review evaluates EEG-based BCI paradigms regarding their advantages and disadvantages from a variety of perspectives. For each paradigm, various EEG decoding algorithms and classification methods are evaluated. The applications of these paradigms with targeted patients are summarized. Finally, potential problems with EEG-based BCI systems are discussed, and possible solutions are proposed.

Brain-computer interface (BCI) technology holds promise to restore the communication and control ability of individuals with severe motor disabilities (Wolpaw et al. 2002). An EEG-based BCI system that detects the P300 event-related potential (ERP) allows users to select items from a matrix consisting of letters, numbers, and function calls (after the method of Donchin et al., 2000) using brain signals rather than the brain’s normal output pathways of peripheral nerves and muscles. Our laboratory seeks to realize independent home use of P300-based BCI by severely disabled individuals. In an earlier study, we found that P300-based BCI performance (i.e., accurate classification) on test data was correlated with the test data and was not correlated with the training data (Mak et al. 2009). The present study set out