What's New at OpenBCI
Hello friends! This month we want to highlight those involved in the back-to-school transition. Without the continual efforts of instructors, teachers, school staff, and parents at the best of times, and certainly in these times, crucial academic instruction wouldn't be possible. We thank them for their courage and contributions. Look out for next month's teacher-appreciation discount code, our small gesture.
Each month we try to bring new educational materials and content to the open-source community, whether it's tutorials for facial, ocular, and limb EMG, or our continually updated ECG and stand-alone EMG tutorials. Thanks to the collaborative efforts of Fan Li and other OpenBCI team members, a fun new tutorial for running video experiments was added to the Learning Section. Follow the step-by-step instructions to start designing your own experiment and make your own external trigger button using OpenBCI hardware!
This Month:
- Dryodes BACK IN STOCK
- Research Highlight
- Community Members of the month
1. IDUN Dryodes are Back in Stock!
  
We're excited to announce the IDUN Dryode™ Alpha is re-stocked and shipping worldwide!
The Dryode™ Alpha combines the best qualities of wet electrodes with the convenience of dry electrodes. It offers comfort and excellent signal quality, making it perfect for extended use.
The electrode has been scientifically evaluated for signal quality, incidence of artifacts, sensitivity and other metrics. In June 2020, the Mobile Health Systems Lab of ETH Zurich published a paper featuring the Dryode™ Alpha for sleep EEG research. "A Protocol for Comparing Dry and Wet EEG Electrodes During Sleep" was accepted to the Frontiers of Neuroscience, Neural Technology.
The Dryode™ Alpha's versatility means it can be used for any EMG/ECG application. Head over to example projects, created by Eva Esteban and other OpenBCI team members, for step-by-step tutorials on how to use these awesome new electrodes!
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2. Research Highlight
Sleepiness/alertness detection systems have long been a keen interest of researchers. There is a strong push for safety in this field. There are a number of systems like electroencephalography-based sleepiness detection system (ESDS), vehicle based sleepiness estimator system, image acquisition technology and bio-mathematical models to detect drowsiness of drivers. However, there has been less research on a combination of these systems to detect sleepiness of drivers.
Thus, the OpenBCI Research Highlight in September goes to "Multimodal System to Detect Driver Fatigue Using EEG, Gyroscope, and Image Processing" published by Naveen Senniappan Karuppusamy and Bo-Yeong Kang, pictured below.
Methods:
The researchers used the OpenBCI Cyton 8-channel Board, and the Ultracortex Mark IV EEG Headset to sample and transmit EEG signal from three subjects. They implemented a new neural network-based hybrid multimodal system that detects driver fatigue using electroencephalography(EEG) data, gyroscope data and image processing data.
Takeaways:
The EEG + gyroscope + image processing hybrid system performed well with a detection accuracy of 93.91% in identifying the drowsiness state of the driver, a higher rate than that of individual detection methods.
Head to the paper to learn more about the methodology!
Learn how OpenBCI products have been used to conduct neuroscience research since 2015 with the OpenBCI Research Collection, a resource we update daily or weekly. If you'd like to submit your paper for consideration, don't hesitate to reply to this email or reach out to contact@openbci.com.
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3. Community Members of the Month
This month we want to celebrate one of the first publications to emerge out of the OpenBCI Sponsorship program! Based in Obafemi Awolowo University, Department of Electronic & Electrical Engineering, the team lead by Kayode Ayodele published "The performance of a low-cost bio-amplifier on 3D human arm movement reconstruction," viewable at PubMed.gov.
Method:
Using the OpenBCI CytonDaisy Board and OpenBCI electrodes, they collected eleven scalp electrode locations from three subjects, with sampling rate of 125 Hz, subsequently band-pass filtered from 0.5 to 40 Hz. After segmentation into epochs, information-rich frequency ranges were determined using filter bank common spatial filter. Simultaneously, the actual hand motions of subjects were captured using a Microsoft Kinect sensor. Multimodal data streams were synchronized using the lab streaming layer (LSL) application. A modified version of an existing multiple linear regression models was employed to learn the relationship between the electroencephalography (EEG) feature input and the recorded kinematic data. To assess system performance with limited data, 10-fold cross validation was used.
Takeaways:
This study has shown that low-cost bio-amplifiers such as the OpenBCI can be used for 3D motion reconstruction tasks. The results compare favourably with a cross-section of existing results, while cost-per-electrode costs were 76% lower than the average per-electrode cost for similar systems and 44% lower than the cheapest previously-reported system.
Our objective since day one has been to help expand access to scientific tools, and we believe this publication is the first of many research endeavors that will be made possible by the OpenBCI Sponsorship Program. Have an idea and an executable plan? Apply for the Program! Applications are reviewed on a rolling basis. We look forward to hearing from you!
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Thank You!
From everyone at OpenBCI, we want to say thank you. The inertia for the emerging technologies we work with is snowballing, and we're excited to keep you in the loop! Your support is making the open-source Neuro-revolution possible. We look forward to getting the latest OpenBCI technology into your hands. If you have any questions, comments, or concerns, don’t hesitate to reach out: contact@openbci.com.
From the OpenBCI team, we heart you!
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