Motor-activity-related mental tasks are widely adopted for brain-computer interfaces (BCIs) as they are a natural extension of movement intention, requiring no training to evoke brain activity. The ideal BCI aims to eliminate neuromuscular movement, making motor imagery tasks, or imagined actions with no muscle movement, good candidates. This study explores cortical activation differences between motor imagery and motor execution for both upper and lower limbs using functional near-infrared spectroscopy (fNIRS). Four simple finger- or toe-tapping tasks (left hand, right hand, left foot, and right foot) were performed with both motor imagery and motor execution and compared to resting state. Significant activation was found during all four motor imagery tasks, indicating that they can be detected via fNIRS. Motor execution produced higher activation levels, a faster response, and a different spatial distribution compared to motor imagery, which should be taken into account when designing an imagery-based BCI. When comparing left versus right, upper limb tasks are the most clearly distinguishable, particularly during motor execution. Left and right lower limb activation patterns were found to be highly similar during both imagery and execution, indicating that higher resolution imaging, advanced signal processing, or improved subject training may be required to reliably distinguish them.

Abstract-This work investigates the potential of a fourclass motor-imagery-based brain-computer interface (BCI) using functional near-infrared spectroscopy (fNIRS). Four motor imagery tasks (right hand, left hand, right foot, and left foot tapping) were executed while motor cortex activity was recorded via fNIRS. Preliminary results from three participants suggest that this could be a viable BCI interface, with two subjects achieving 50% accuracy. fNIRS is a noninvasive, safe, portable, and affordable optical brain imaging technique used to monitor cortical hemodynamic changes. Because of its portability and ease of use, fNIRS is amenable to deployment in more natural settings. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) BCIs have already been used with up to four motor-imagery-based commands. While fNIRS-based BCIs are relatively new, success with EEG and fMRI systems, as well as signal characteristics similar to fMRI and complementary to EEG, suggest that fNIRS could serve to build or augment future BCIs.

Music is an integral part of high school students' daily lives, and most use digital music devices and services. The oneweek Summer Music Technology (SMT) program at Drexel University introduces underclassmen high school students to music technology to reveal the influence and importance of engineering, science, and mathematics. By engaging participants' affinity for music, we hope to motivate and catalyze curiosity in science and technology. The curriculum emphasizes signal processing concepts, tools, and methods through hands-on activities and individual projects and leverages computer-based learning and open-source software in most activities. Since the program began in 2006, SMT has enrolled nearly 100 high school students and further developed the communication and teaching skills of nearly 20 graduate and undergraduate engineering students serving as core instructors. The program also serves to attract students from backgrounds under-represented in engineering, math, and science who may not have considered these fields.

Motor-imagery tasks are a popular input method for controlling brain-computer interfaces (BCIs), partially due to their similarities to naturally produced motor signals. The use of functional near-infrared spectroscopy (fNIRS) in BCIs is still emerging and has shown potential as a supplement or replacement for electroencephalography. However, studies often use only two or three motor-imagery tasks, limiting the number of available commands. In this work, we present the results of the first four-class motor-imagery-based online fNIRS-BCI for robot control. Thirteen participants utilized upper- and lower-limb motor-imagery tasks (left hand, right hand, left foot, and right foot) that were mapped to four high-level commands (turn left, turn right, move forward, and move backward) to control the navigation of a simulated or real robot. A significant improvement in classification accuracy was found between the virtual-robot-based BCI (control of a virtual robot) and the physical-robot BCI (control of the DARwIn-OP humanoid robot). Differences were also found in the oxygenated hemoglobin activation patterns of the four tasks between the first and second BCI. These results corroborate previous findings that motor imagery can be improved with feedback and imply that a four-class motor-imagery-based fNIRS-BCI could be feasible with sufficient subject training.

David Rosen is a doctoral student in Drexel University's Applied Cognitive and Brain Sciences program. He has an M.S degree in Teaching and Instruction and several years of experience as a public school educator. Working in the Music and Entertainment Technology (MET-Lab) and Creativity Research Lab, his interdisciplinary research explores the underlying cognitive mechanisms and factors of creativity, expression, insight, and flow, specifically within the domain of music performance and improvisation. He has also worked on several research projects which attempt to infuse, design, and evaluate various pedagogical methodologies to enhance creativity and creative problem solving in the classroom. Introduction to STEAM through Music Technology (Evaluation) AbstractReal-world problem solving across domains in the 21st century requires technical knowledge and skills, as well as creative thinking and problem solving; however, the pedagogy of many STEM education programs only focuses on the technical aspects of their discipline. The point at which students are first introduced to various STEM fields is critical in terms of their interest, motivation, and understanding of potential applications. These early years greatly impact the decision of whether a student pursues a career or major in a STEM field. Thus, teaching methodologies for young STEM students must balance, or better yet, intertwine core concepts and knowledge with student engagement through hands-on, project-based learning and connections to topics of interest, such as music and the arts. Too often, STEM pedagogy paints a picture of a world where problems have convergent solutions, in contrast with a reality where optimal solutions are divergent in nature, requiring creativity, originality, and insight. In order to revitalize and reimagine STEM learning, there must be true integration of the arts and creative thinking in the sciences, debunking the traditional approach of STEM and the arts being dichotomous. Through the Summer Music Technology (SMT) program at Drexel University for rising high school sophomores and juniors, we aim to illustrate the interconnectedness of music with engineering, science, and mathematics through inquiry-based modules and projects involving creative problem solving and self-expression. Our approach not only serves to emphasize creativity amongst the technically inclined, but also, presents STEM in an accessible, engaging way, leveraging students' passion and interest in music as a catalyst for learning. SMT is a unique STEM experience for high-school students who would not otherwise consider supplementing their education with STEM or even pursuing STEM careers.