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People who suffer from amyotrophic lateral sclerosis (ALS) eventually lose all voluntary muscle control. In the late stages of the disease, traditional augmentative and alternative communication (AAC) devices fail to provide adequate levels of communication. Brain-computer interface (BCI) technology has provided effective communication after all other AAC devices have failed. Nonetheless, EEG-based BCI devices may also fail for people with late-stage ALS due to loss of voluntary eye movement. Specifically, some people may suffer from random eye movement (nystagmus) and/or drooping of the eyelids (ptosis). Presently, it is unclear in the literature whether BCI operation requires voluntary control of eye movement. The current study attempts to simulate involuntary random eye movement in able-bodied individuals employing the P300-based BCI. To simulate involuntary random eye movement, the stimuli shift in the X and Y dimensions. Stimulus movement ‘Jitter’ occurs between each stimulus presentation in increments of 1-5 pixels (Jitter 1), 5-10 pixels (Jitter 2), 10-15 pixels-(Jitter 3), or a no movement control condition. Data collected from a previous study using 22 participants compared the control condition to Jitter 1 and Jitter 2 indicated higher accuracy for control and Jitter 1 than Jitter 2. No significant differences were found in accuracy, selections per minute, or bitrate. Waveform analysis indicated significantly higher P300 amplitude for the control condition and Jitter 1 than Jitter 2. Preference survey scores showed a preference for Jitter 1 as compared to control and Jitter 2. This finding was unexpected and may be due to the slight movement of Jitter 1 forcing participants to be vigilant, but not distracted. Based on our finding in this study, the current study examines the amount of pixel movement that could lead to reductions in performance. Participants completed a control condition and the three levels of Jitter in a counter-balanced design. Preliminary data for the current study was collected from 15 participants. No significant differences were observed between the three conditions in measures of BCI accuracy, selections per minute, and bitrate. Furthermore, preference survey scores indicated no significant difference in condition preference. Based on the findings of the first study, as well as the data collected so far in the current study, it appears that random movement does not have a significant impact on the ability of healthy participants to operate the BCI system. This could indicate that individuals with random eye movement should be able to operate the system with high rates of accuracy.

Brain-Computer Interfaces (BCIs) that rely on motor imagery currently allow subjects to control quad-copters, robotic arms, and computer cursors. Recent advancements have been made possible because of breakthroughs in fields such as electrical engineering, computer science, and neuroscience. Currently, most real-time BCIs use hand-crafted feature extractors, feature selectors, and classification algorithms. In this work, we explore the different classification algorithms currently used in electroencephalographic (EEG) signal classification and assess their performance on intracranial EEG (iEEG) data. We first discuss the motor imagery task employed using iEEG signals and find features that clearly distinguish between different classes. Second, we compare the different state-of-the-art classifiers used in EEG BCIs in terms of their error rate, computational requirements, and feature interpret-ability. Next, we show the effectiveness of these classifiers in iEEG BCIs and last, show that our new classification algorithm that is designed to use spatial, spectral, and temporal information reaches performance comparable to other state-of-the-art classifiers while also allowing increased feature interpret-ability. / Master of Science / Brain-Computer Interfaces (BCIs) as the name suggests allows individuals to interact with computers using electrical activity captured from different regions of the brain. These devices have been shown to allows subjects to control a number of devices such as quad-copters, robotic arms, and computer cursors. Applications such as these obtain electrical signals from the brain using electrodes either placed non-invasively on the scalp (also known as an electroencephalographic signal, EEG) or invasively on the surface of the brain (Electrocorticographic signal, ECoG). Before a participant can effectively communicate with the computer, the computer is calibrated to recognize different signals by collecting data from the subject and learning to distinguish them using a classification algorithm. In this work, we were interested in analyzing the effectiveness of using signals obtained from deep brain structures by using electrodes place invasively (also known as intracranial EEG, iEEG). We collected iEEG data during a two hand movement task and manually analyzed the data to find regions of the brain that are most effective in allowing us to distinguish signals during movements. We later showed that this task could be automated by using classification algorithms that are borrowed from electroencephalographic (EEG) signal experiments.

There are hundreds of thousands of people who could benefit from a Brain-Computer Interface. However, not all are willing to undergo surgery, so an EEG is the prime candidate for use as a BCI. The features of Event-Related Desynchronization and Synchronization could be used for a switch and have been in the past. A new method of feature selection was proposed to optimize classification of active motor movement vs a non-active idle state. The previous method had pre-selected which frequency and electrode to use as electrode C3 at the 20Hz bin. The new method used SPSS statistical software to determine the most significant frequency and electrode combination. This improved method found increased accuracy in classifying cases as either active or idle states. Future directions could be using multiple features for classification and BCI control, or exploiting the difference between ERD and ERS, though for either of these a more advanced algorithm would be required.

Thesis advisor: Robert Signorile / It has long been known that as neurons fire within the brain they produce measurable electrical activity. Electroencephalography (EEG) is the measurement and recording of these electrical signals using sensors arrayed across the scalp. Though there is copious research in using EEG technology in the fields of neuroscience and cognitive psychology, it is only recently that the possibility of utilizing EEG measurements as inputs in the control of computers has emerged. The idea of Brain-Computer Interfaces (BCIs) which allow the control of devices using brain signals evolved from the realm of science fiction to simple devices that currently exist. BCIs naturally present themselves to many extremely useful applications including prosthetic devices, restoring or aiding in communication and hearing, military applications, video gaming and virtual reality, and robotic control, and have the possibility of significantly improving the quality of life of many disabled individuals. However, current BCIs suffer from many problems including inaccuracies, delays between thought, detection, and action, exorbitant costs, and invasive surgeries. The purpose of this research is to examine the Emotiv EPOC© System as a cost-effective gateway to non-invasive portable EEG measurements and utilize it to build a thought-based BCI to control the Parallax Scribbler® robot. This research furthers the analysis of the current pros and cons of EEG technology as it pertains to BCIs and offers a glimpse of the future potential capabilities of BCI systems. / Thesis (BA) — Boston College, 2010. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Computer Science Honors Program. / Discipline: Computer Science.

Brain-computer interface (BCI) technologies allow users to control external devices through brain activity alone, circumventing the somatic nervous system and the need for overt physical movement. BCIs may potentially benefit individuals with severe neuromuscular disorders who experience significant, and often total, loss of voluntary muscle control (e.g. amyotrophic lateral sclerosis, multiple sclerosis, brainstem stroke). Though a majority of BCI research to date has focused on electroencephalography (EEG) for brain signal acquisition, recently researchers have noted the potential of an optical imaging technology called near-infrared spectroscopy (NIRS) for BCI applications. This thesis investigates the feasibility of a practical, online optical BCI based on conscious modulation of prefrontal cortex activity through the performance of different cognitive tasks, specifically mental arithmetic (MA) and mental singing (MS). The thesis comprises five studies, each representing a step toward the realization of a practical optical BCI. The first study demonstrates the feasibility of a two-choice synchronized optical BCI based on intentional control states corresponding to MA and MS. The second study explores a more user-friendly alternative - a two-choice system-paced BCI supporting a single intentional control state (either MA or MS) and a natural baseline, or "no-control (NC)", state. The third study investigates the feasibility of a three-choice system-paced BCI supporting both MA and MS, as well as the NC state. The fourth study examines the consistency with which the relevant mental states can be differentiated over multiple sessions. The first four studies involve healthy adult participants; in the final study, the feasibility of optical BCI use by a user with Duchenne muscular dystrophy is explored. In the first study, MA and MS were classified with an average accuracy of 77.2% (n=10), while in the second, MA and MS were differentiated individually from the NC state with average accuracies of 71.2% and 62.7%, respectively (n=7). In the third study, an average accuracy of 62.5% was obtained for the MA vs. MS vs. NC problem (n=4). The fourth study demonstrated that the ability to classify mental states (specifically MA vs. NC) remains consistent across multiple sessions (p=0.67), but that there is intersession variability in the spatiotemporal characteristics that best discriminate the states. In the final study, a two-session average accuracy of 71.1% was achieved in the MA vs. NC classification problem for the participant with Duchenne muscular dystrophy.

Individuals with limited motor control are often unable to interact with their environment. Recently, near-infrared spectroscopy (NIRS) systems have been investigated as potential brain-computer interfaces (BCI). Previous studies examined data offline, preventing users from understanding how their thoughts triggered the NIRS system. This thesis focused on understanding the short-term effects of feedback on user’s ability to learn how to control BCIs. Data were collected from control and experimental groups over seven sessions, as they performed fast singing imagery or mental arithmetic. Significant differences were observed between the control group’s results in non-feedback sessions and the experimental group’s results in feedback sessions. Qualitative results from 3 of the 10 participants suggested they had control of the feedback system. They performed the task with online accuracies of 61% - 88% in the final 2 sessions with feedback. These results suggest that continued investigation of NIRS feedback systems is warranted.

Near-infrared spectroscopy (NIRS) is an emerging non-invasive brain-computer interface (BCI) modality that measures changes in hemoglobin concentrations in neurocortical tissue. Previous NIRS studies have not employed real-time feedback with online classification, a combination which would allow users to alter their mental strategy on the fly. This thesis reports the results of two online studies. The first study contrasted online classification of prefrontal hemodynamics using an artificial neural network (ANN) and a hidden Markov model-based (HMM) classifier. The second study measured the accuracy of an online linear discriminant classifier. In study 1, only the ANN classifier facilitated online classification rates greater than chance (p=0.0289). In study 2, a new feedback system and experimental protocol led to improved classification rates over those of the first study (p=5.1*10^(-5)). While control over instantaneously generated feedback in online NIRS-BCIs has been demonstrated, factors such as user frustration, mental fatigue, and restrictions on ambient lighting may compromise performance.

Severe motor impairments can severely restrict interaction with one's surroundings. Brain computer interfaces combined with text-based communication systems, such as the P300 Speller, have allowed individuals with motor disabilities to spell messages with their EEG signals. Although providing full composition flexibility, they enable communication rates of only a few characters per minute. Utterance-based communication systems have been developed for individuals with disability and have greatly increased communication speeds, but have yet to be applied to BCIs. This paper proposes an utterance-based communication system using the P300-BCI in which words are organized in a network structure that facilitates rapid retrieval. In trials with able-bodied participants, the proposed system achieved greater message speeds, but rated lower in effectiveness than the P300 Speller. Nonetheless, subject preferences and reports of self-perceived effectiveness suggested an inclination towards the proposed word system and thus further investigation of word-based networks is warranted in brain-controlled AAC systems.

Near-infrared spectroscopy (NIRS) is an emerging non-invasive brain-computer interface (BCI) modality that measures changes in hemoglobin concentrations in neurocortical tissue. Previous NIRS studies have not employed real-time feedback with online classification, a combination which would allow users to alter their mental strategy on the fly. This thesis reports the results of two online studies. The first study contrasted online classification of prefrontal hemodynamics using an artificial neural network (ANN) and a hidden Markov model-based (HMM) classifier. The second study measured the accuracy of an online linear discriminant classifier. In study 1, only the ANN classifier facilitated online classification rates greater than chance (p=0.0289). In study 2, a new feedback system and experimental protocol led to improved classification rates over those of the first study (p=5.1*10^(-5)). While control over instantaneously generated feedback in online NIRS-BCIs has been demonstrated, factors such as user frustration, mental fatigue, and restrictions on ambient lighting may compromise performance.