Bongjae Choi, Sungho Jo Presented by: Yanrong Wo A Low-Cost EEG System-Based Hybrid Brain-Computer Interface for Humanoid Robot Navigation and Recognition Bongjae Choi, Sungho Jo Presented by: Yanrong Wo

Background EEG BCI : Electroencephalogram Brain Computer Interface Non-invasive Two Types of BCI Protocols: Active Reactive Non-invasive: easy, safe, but noisier/less accurate Active: Consciously think about something Reactive: brain signals related to an external stimuli Active - more natural Reactive - less training/simpler implementation

Active BCI Protocol ERD: Event Related Desynchonization Amplitude Decrease ERS: Event Related Synchronization Amplitude Increase Motor Imagery Thinking about performing an action without executing it Motor Imagery: Used by sports psycologists too

Reactive BCI Protocols P300 Potential wave caused by decision making Amplitude increases after a target stimulus 300 ms delay SSVEP Steady state visually evoked potential Response to visual stimuli at a certain frequency SSVEP- Looking at certain frequency causes your brain to send signals at multiples of that frequency

What’s the goal? Use a low-cost BCI system to navigate a humanoid robot through a maze from a designated start to a designated goal and along the way detect if there any images of the user’s favorite object.

The equipment Emotiv Epoc BCI Easy to use, portable, simple to operate 14 channels sampling at a frequency of 128Hz Nao Humanoid 25 DOF Monocular vision on its head 3.3cm/s walking speed, 0.13 rad/s turning speed (configured)

Is it cheap? BioSemi ActiveTwo “8 channels hardware complete with active electrodes, plus acquisition software: approx: EUR 13,500” “256+8 channels hardware complete with headcaps and active electrodes, plus acquisition software: approx. EUR 75,000” 13500 Euro = $14500 75000 = 80500

What’s the difference? 2015 paper

The experiment Two modes: Exploration and Navigation SSVEP and ERD Recognition P300

Exploration and Navigation Moving forward and body turns (left/right) Exploration Head turns (left/right) Visual feedback during both ERD to switch between exploration and navigation modes SSVEP for left/right head turns ERD motor imagery can be anything, hand or foot, foot encouraged User doesn’t control body turns - if body and head are misaligned by more than 9 degrees, switching into exploration mode will align them

Exploration and Navigation

Recognition Mode Objects in view are outlined in a box User centers the view on the object(s) Blue window border indicates recognition mode 4s rest, before each object randomly flashed for 250ms (random order) Epoch of signals for that object is 600ms long starting from flashing (Recall that P300 has a 300 ms delay) Blue window border disappears indicating end of recognition mode Place center of object(s) in center of screen

Recognition Mode

BCI Protocols ERD-based protocol Common Spatial Patterns SVM SSVEP-based protocol Canonical Correlation Analysis Selection of SSVEP vs ERD P300-based Protocol xDAWN spatial filters Bayesian linear discriminant analysis classifier Dynamic Fading Feedback Rule CSP - extract features SVM - support vector machine

Candidate Selection Criteria Between 20-30 years of age Same gender (male) Same laterality (right handed) No central nervous system abnormalities Not taking psychiatric medications No history of epilepsy, dyslexia, or hallucinations No previous BCI experience 5 healthy male volunteers (age 23.4 土 3.8

The whole thing

Video http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0074583#s5

The Results All subjects took more time (avg 1.22) Took less steps and explored more Two methods are comparable

Conclusions and Future Work Has potential Lower quality data, but worked Apply to more practical applications Make usage more user friendly

References BioSemi. "Applications: N2pc Effect." Applications: N2pc Effect. N.p., n.d. Web. 02 Apr. 2017. BioSemi. "Frequently Asked Questions." Biosemi EEG ECG EMG BSPM NEURO Amplifiers Systems. N.p., n.d. Web. 02 Apr. 2017. Choi B, Jo S (2013) A Low-Cost EEG System-Based Hybrid Brain-Computer Interface for Humanoid Robot Navigation and Recognition. PLoS ONE 8(9): e74583. doi:10.1371/journal.pone.0074583 Emotive. "Headsets Archives." Emotiv. N.p., n.d. Web. 02 Apr. 2017. Martinez-Leon, Juan-Antonio, Jose-Manuel Cano-Izquierdo, and Julio Ibarrola. "Are Low Cost Brain Computer Interface Headsets Ready for Motor Imagery Applications?" Expert Systems with Applications 49 (2016): 136-44. Web.

Thanks! Questions?