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Brain-computer interfaces (BCI) technology can provide an alternative form of communication for individuals who lose their ability to communicate due to stroke, brain injury, or from amyotrophic lateral sclerosis (ALS). One of the most studied BCI's is known as the P300 Speller. The P300 Speller presents a matrix of letters, numbers, and computer commands (8x9 in the current study). Each item in the matrix flashes at a rapid rate and the participants' task is to count the number of times the desired letter flashes. When the attended letter flashes a P300 event-related potential will occur. The BCI can detect which stimulus produced a P300 and provide the stimulus as feedback to the participant. A primary goal of BCI research is to improve the speed and accuracy of the system by manipulating stimulus presentation parameters. One such parameter is the inter-stimulus-interval (ISI), which is the time between the offset of one stimulus (i.e., character in a grid of letters and numbers) and the onset of another stimulus. Previous studies have examined the effect of varying the ISI; however, to date, no study has included a condition that has no ISI between stimuli. This study compares BCI speed, accuracy, and bit rate from a condition that has no ISI to a condition that has an ISI of 62.5ms. In addition to evaluating performance, the current study also evaluates which of the two conditions is preferred by the participant. Preliminary data have been collected from seven able bodied participants. Each participant completed No-ISI and a 62.5ms ISI spelling task (counter-balanced). Before each spelling task a calibration period including eighteen characters (three six letter words) was conducted. After calibration, the participant was presented with an additional 18 characters and the computer provided online feedback indicating whether or not the BCI selected the correct character. Performance data were analyzed using t-tests. The No-ISI condition produced significantly higher bit rate than the 62.5ms ISI condition (p=0.039). No differences in accuracy (i.e., number of correct selections) or selections per minute (i.e., letters typed per minute) were observed, p=0.106 and p=0.088, respectively. In regard to the preference data, no significant difference was observed between the No-ISI and 62.5ms ISI condition (p=0.356). These data indicate that a No-ISI P300 BCI is a viable alternative to paradigms that included an ISI between sequential flashes of the matrix items. In addition, the results yielded higher bit rates and no difference in preference was observed. Based on these preliminary data, we suggest that the No-ISI paradigm should be tested in participants who have severe communication disorders.

Non-invasive Brain Computer Interface (BCI) technology can be incredibly important for those who are no longer able to communicate to due loss of muscle control. However, in comparison to other methods of non-muscular communication, such as an eye tracker, the BCI method of communication can be slow. Therefore it is important to implement techniques that can improve both accuracy and speed of BCI performance. Currently, the P300 Speller allows communication at a rate up to eight selections per minute. Given this relatively slow rate of communication highly accurate classification is of great importance. One method of improving accuracy in BCI performance has been the presentation of familiar faces rather than traditional letter flashes or character flashes. Previous studies have shown that the use of faces elicits both an N400 and N170 component in conjunction with the P300 component, resulting in greater speed and accuracy of letter selection. Functional MRI studies have shown that images of familiar locations produce unique brain responses located in distinct brain regions (i.e. parahippocampal place area). These two types of stimuli (images of faces and images of familiar locations) were incorporated into simultaneous two-stimulus presentation paradigm with the intention of developing two distinct classifiers to discriminate between the cognitive responses produced by the spatially disparate areas. By developing stimulus specific classifiers, the BCI system could eliminate half of the characters in the matrix as potential selections, thus reducing the amount of error in performance. The present study aims to provide proof of concept. Ten able-bodied participants completed one experimental session consisting of two calibration phases. Each participant completed two calibration phases: one for face stimuli and one for house stimuli. In each calibration phase, participants spelled three-six letter words using an 8x9 matrix of alphanumeric characters and symbols. During each calibration phase, only one of the two images were presented to the participant (i.e., faces only or houses only). Participants then completed a copy spelling portion that consisted of spelling six words containing six characters each. During this time, participants received feedback in regard to the accuracy of the BCI systems character selections. Participants were instructed to continue spelling the word without correcting errors made by the BCI system. Offline analyses were conducted to examine BCI accuracy, selections per minute, and bitrate for stimulus specific classifiers. Results indicated no significant differences in accuracy; however, results showed a significant interaction of classifier (i.e., face or house classifier) by image type (i.e., face or house) for selections per minute and bitrate. Specifically, the house classifier produced higher selections per minute and bit rate when applied to house data than the house classifier applied to face data, and vice versa for the face classifier. These results indicate that stimulus specific classifiers may be able to eliminate half of the characters located in the matrix as potential character selections, thereby increasing overall BCI performance.

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. Braincomputer 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). The current study is the first attempt to simulate involuntary random eye movement in able-bodied individuals. This study employs the P300-based BCI. The system presents a matrix (6x6 in this study) of letters and numbers to participants on the computer screen and their task is to focus attention to a specific item within the matrix. The attended item of the matrix will produce a P300 event-related potential (ERP). The BCI determines which item produced the largest P300 ERP and presents (types) this item on the computer screen. To simulate involuntary random eye movement the 6x6 matrix would move in random directions in increments of 1–5 pixels “Jitter 1” or increments of 10–15 pixels “Jitter 2”. Movement (i.e., jitter) occurs during the inter-stimulus interval (ISI), which is the time between the offset of one character flash and the onset of another character flash. The matrix can move in the X dimension, Y dimension, or in both dimensions simultaneously. Participants complete two conditions: 1) control (i.e., no jitter) and 2) one of the two Jitter conditions (counter-balanced). Prior to each condition, each participant completed a calibration phase with eighteen character selections (three six letter words). Following calibration, each participant was presented with 18 more characters and the computer provided online feedback to indicate if the BCI selected the character the participant intended for it to choose. To date, six participants have completed the experiment. Three participants completed the control condition and Jitter 1 and three completed the control and Jitter 2. Preliminary data indicate highest accuracy (number of characters selected correctly) in Jitter 1 (100%), followed by Control (94%), and lowest accuracy in Jitter 2 (81%). In addition, participants were surveyed in regard to distraction and attentional focus. Surveys indicate that participants in Jitter 1 found it less distracting and easier to focus in Jitter 1 than control. Whereas, participants in Jitter 2 found it more distracting and harder to focus in Jitter 2 as compared to control. The data indicate that modest amounts of matrix movement may not be deleterious to performance, and may in fact improve performance through increasing attentional resources to the task. In Jitter 2 a decline in performance was observed; however, accuracy was still adequate for effective communication.

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