This document describes an auditory brain-computer interface for navigation using event-related potential classification. The system uses an auditory oddball paradigm with 5 speakers around the user to provide navigation commands. Experimental conditions included discrete and continuous navigation modes. For discrete navigation, a 5x5 classification buffer and linear discriminant analysis were used to classify commands for individual movements. For continuous navigation, a penalized and sigmoid-transformed classification buffer along with matrix multiplication were used to map classifications to continuous movement directions. Performance results showed the discrete condition had more optimal trajectories while the continuous condition was found more enjoyable by subjects.

1. Auditory BCI for Navigation
using ERP classification
By Mario Vivero
1

2. Outline
•
•
•
•
•
Description
Methodology
Results
Summary and Conclusion
Questions
2

3. AMUSE paradigm
AMUSE
paradigm
Stimulation
Setup
Characters chosen
in two rounds. 6class selection
Adapted from: http://www.tobiproject.org/sites/default/files/public/Public
ations/TOBI-159.pdf
3

4. Navigational Environment
Upper Perspective
Custom designed maze. All textures and 3D
objects were downloaded from:
http://www.turbosquid.com
4

5. Auditory Navigational BCI based
on the AMUSE paradigm
Speaker #1
Speaker #5
Navigational Setup
for Acoustic
Stimulation
Speaker #4
Speaker #2
Speaker #3
1.- Forward Movement. 2.- Forward Right
Movement. 3.- Turn Right. 4.- Turn Left.
5.- Forward Left Movement
5

6. Experimental Conditions
Discrete Condition
Auditory
Stimulation (5
Full Rounds)
Signal
Processing &
Classification
Control
Strategy
Discrete
Movement
…
Auditory
Stimulation
(5 Rounds)
Signal
Processing &
Classification
6

7. Experimental Conditions
Discrete Condition
Auditory
Stimulation (5
Full Rounds)
Signal
Processing &
Classification
Control
Strategy
Discrete
Movement
…
Auditory
Stimulation
(5 Rounds)
Signal
Processing &
Classification
Continuous Condition
Continuous
Stimulation
Signal
Processing &
Classification
Control
Strategy
Continuous
Movement
…
Continuous
Stimulation
Signal
Processing &
Classification
7

8. Experimental Conditions
Discrete Condition
Auditory
Stimulation (5
Full Rounds)
Signal
Processing &
Classification
Control
Strategy
Discrete
Movement
…
Auditory
Stimulation
(5 Rounds)
Signal
Processing &
Classification
Continuous Condition
Continuous
Stimulation
Signal
Processing &
Classification
Control
Strategy
Continuous
Movement
…
Continuous
Stimulation
Signal
Processing &
Classification
Joystick - Benchmark Condition
Input
Movement
Control
Strategy
Continuous
Movement
8

9. Auditory Stimulation and
Signal Processing
Each stimulus lasted 40 ms. SOA
was set to 175 ms.
The calibration phase consisted of an auditory
oddball task without visual feedback.
Binary classification of target and non-target epochs was
performed using a Linear Discriminant Analysis (LDA). Due
to the data high dimensionality a shrinkage method was
also applied.
9

10. Control Strategy
D ∈ R CXB
W ∈ R CXB
Penalization
D’ = D ⊙ W
W=
Continuous
No Penalization - Discrete
10

11. Control Strategy
D ∈ R: C X B
W ∈ R: C X B
Penalization
Sigmoid Function
Transformation
D’ = D ⊙ W
Continuous
W=
No Penalization - Discrete
D’’ = f (D’, a, c)
11

12. Control Strategy
D ∈ R CXB
W ∈ R CXB
Penalization
D’ = D ⊙ W
Continuous
W=
No Penalization - Discrete
D’’ = f (D’, a, c)
Sigmoid Function
Transformation
Re-mapping
Out =
V=
-
Forward
Forward-Right
Right Turn
Left Turn
Forward-Left
12

13. Performance
Continuous Condition
Discrete Condition
Custom designed maze. All textures and 3D objects were downloaded from: http://www.turbosquid.com.
Full Videos can be found on: http://www.youtube.com/watch?v=DUbqThGLykg
http://www.youtube.com/watch?v=dOcuLeYRzxE
13

14. ERP Comparison
Calibration Data ERP Grand Average
Cz
Calibration
Mean of
Accuracy: 71%
according to
crossvalidation
14

15. Path Lengths and Times
First room
15

16. Data Spread at 37 seconds
16

17. Summary and Conclusions
• The Discrete condition has a more
optimal trajectory.
• Time spent in rooms is the same
for both conditions
• According to subject feedback the
continuous condition was found
more enjoyable
17

18. A more intelligent control strategy
algorithm will improve the overall
performance.
18

19. Thank you
19

20. 20

21. • D = Data = 5*5 matrix stored in buffer
5 X 5 matrix of ones
• W = Window
If discrete
If continuous
• m = number of classes / speakers
• n = desired dimensions = 2 for X and Y
• Sigmoid membership function(z, a, c) = g(z, a, c) =
V=
-
Forward
Forward-Right
Right Turn
Left Turn
Forward-Left
OUTPUT =
OUTPUT =
21

22. Continuous Control Strategy
Penalization
ELEMENTWISE
MULTIPLICATION
X
Classes /
Speakers
=
Penalized Buffered Data
Iterations
22

23. Continuous Control Strategy
(cont. 2)
Sigmoid Function
Transformation
Sigmoid
membership
function
ELEMENTWISE
=
Class
Iter.
23

24. Continuous Control Strategy
(cont. 3)
Re-mapping
MATRIX
MULTIPLICATION
.
Forward
Forward-Right
Right Turn
Left Turn
Forward-Left
OUTPUT
Class
=
=
Iter.
24