by Dr. Christina Lavallee
Scientific Consultant (Brain Products)
We are pleased to announce the winners of the second MoBI Award! The aim of the MoBI Award is to recognize excellence in the field of Mobile Brain/Body Imaging research. Given that this is such a rapidly growing field, Brain Products wants to encourage scientists who take their research out of the lab and into the real world. During the
3rd International Mobile Brain/Body Imaging Conference in Berlin, Brain Products awarded the first, second and third place winners with LiveAmp 32, LiveAmp 16 and LiveAmp 8 packages (incl. amplifier, electrode cap and recording software), respectively.
When we closed submissions for the MoBI Award 2018 on April 30th, we were truly overwhelmed by the outstanding quality of the entries. We also received a lot of positive feedback from the independent jury about the exceptional work they were asked to review. Chaired by Prof. Klaus Gramann, all submitted publications were scored based on: positive impact on society, innovation and impact of the research, as well as scientific quality.
The winners were recognized for their impressive contributions to the field of MoBI research during an awards ceremony on July 13th. There, we played a video (see below) from our first-place winner, Joanna E.M. Scanlon, who was unfortunately unable to attend in person. A co-author received the second-place award on behalf of Dr Anderson Oliveira, who was also unfortunately not present. Sarah Blum, the third-place winner delivered a short talk on her award-winning research, which was very well received by the audience.
Congratulations again to the top 3 and many thanks to all the scientists who submitted their work to the 2018 MoBI Award. (See here for the top ranked contributions 2018).
Of course, we would like to also thank all of the jury members for their time and dedication as well as the MoBI Society for providing us the opportunity to hold the award ceremony at the 3rd International Mobile Brain/Body Imaging Conference in Berlin. We are already looking forward to the next instance of the MoBI Award!
Below, we would like to recognize our three winners: Below, we would like to recognize our three winners: Joanna M.E. Scanlon, Dr Anderson Oliveira, and Sarah Blum who compiled short summaries of their papers to share in this Press Release.
If you want to check out more about the MoBI award or MoBI research, in general, please visit: www.mobi-award.com.
Brain Products MoBI Award 2018: 1st place winner
Brain Res. 2017 Dec 14. pii: S0006-8993(17)30544-9. doi: 10.1016/j.brainres.2017.12.010.
[Epub ahead of print]
Taking off the training wheels: Measuring auditory P3 during outdoor cycling using an active wet EEG system
Scanlon JEM, Townsend KA, Cormier DL, Kuziek JWP, Mathewson KE
Objective: Mobile EEG allows for the investigation of brain activity in natural environments. However, before this method can be used more commonly in research we need to fully understand the ways in which the environment can change the way we process stimuli in the world. The purpose of this study was to investigate brain activity during a mobile task in a real-world environment, in comparison to typically isolated laboratory conditions.
Approach: In this study, EEG equipment was adapted for use and transportation in a backpack while cycling. ERPs, spectra, and data noise were evaluated as participants performed an auditory oddball task while either cycling outside or sitting in an isolated chamber inside the lab in different conditions.
Main results: Cycling increased EEG data noise and marginally diminished the power of alpha oscillations. This increased noise, however, did not influence the ability to measure reliable event related potentials (ERP). The P3 was similar in topography and morphology between the two conditions, with a lower amplitude in the outside cycling condition. There was only a minor decrease in the statistical power available to measure reliable ERP effects. Unexpectedly, decreased P2 and increased N1 amplitude were observed while cycling outside, evoked by both standard and target stimuli. As both of these components have been related to selective attention, this may be due to attentional processes filtering the overlapping sounds between the tones used and similar environmental frequencies. In later follow up studies we have been able to replicate this effect with only background sound inside the lab.
Significance: Our findings suggest that one’s environment can change the way we process stimuli in cognitive tasks.
Brain Products MoBI Award 2018: 2nd place winner
J Neurophysiol. 2017 Oct 1;118(4):1943-1951. doi: 10.1152/jn.00926.2016. Epub 2017 Jul 5.
Restricted vision increases sensorimotor cortex involvement in human walking
Oliveira AS, Schlink BR, Hairston WD, König P, Ferris DP
Objective: Electroencephalography (EEG) can assess brain activity during whole-body motion in humans but head motion can induce artifacts that obfuscate electrocortical signals. Definitive solutions for removing motion artifact from EEG have yet to be found, so creating methods to assess signal processing routines for removing motion artifact are needed. We present a novel method for investigating the influence of head motion on EEG recordings as well as for assessing the efficacy of signal processing approaches intended to remove motion
Approach: We used a phantom head device to mimic electrical properties of the human head with three controlled dipolar sources of electrical activity embedded in the phantom. We induced sinusoidal vertical motions on the phantom head using a custom-built platform and recorded EEG signals with three different acquisition systems while the head was both stationary and in varied motion conditions.
Main results: Recordings showed up to 80% reductions in signal-to-noise ratio (SNR) and up to 3600% increases in the power spectrum as a function of motion amplitude and frequency. Independent component analysis (ICA) successfully isolated the three dipolar sources across all conditions and systems. There was a high correlation (r > 0.85) and marginal increase in the independent components’ (ICs) power spectrum (∼15%) when comparing stationary and motion parameters. The SNR of the IC activation was 400%-700% higher in comparison to the channel data SNR, attenuating the effects of motion on SNR.
Significance: Our results suggest that the phantom head and motion platform can be used to assess motion artifact removal algorithms and compare different EEG systems for motion artifact sensitivity. In addition, ICA is effective in isolating target electrocortical events and marginally improving SNR in relation to stationary recordings.
Brain Products MoBI Award 2018: 3rd place
Biomed Res Int. 2017;2017:3072870. doi: 10.1155/2017/3072870. Epub 2017 Nov 16.
EEG Recording and Online Signal Processing on Android: A Multiapp Framework for Brain-Computer Interfaces on Smartphone
Blum S, Debener S, Emkes R, Volkening N, Fudickar S, Bleichner MG
Objective: Our aim was the development and validation of a modular signal processing and classification application enabling online electroencephalography (EEG) signal processing on off-the-shelf mobile Android devices. The software application SCALA (Signal ProCessing and CLassification on Android) supports a standardized communication interface to exchange information with external software and hardware.
Approach. In order to implement a closed-loop brain-computer interface (BCI) on the smartphone, we used a multiapp framework, which integrates applications for stimulus presentation, data acquisition, data processing, classification, and delivery of feedback to the user.
Main results: We have implemented the open source signal processing application SCALA. We present timing test results supporting sufficient temporal precision of audio events. We also validate SCALA with a well-established auditory selective attention paradigm and report above chance level classification results for all participants. Regarding the 24-channel EEG signal quality, evaluation results confirm typical sound onset auditory evoked potentials as well as cognitive event-related potentials that differentiate between correct and incorrect task performance feedback.
Significance: We present a fully smartphone-operated, modular closed-loop BCI system that can be combined with different EEG amplifiers and can easily implement other paradigms.