The 26th Annual CHI Conference on Human Factors in Computing Systems

Feasibility and Pragmatics of Classifying Working Memory Load with an Electroencephalograph

author: Scott E. Hudson, School of Computer Science, Carnegie Mellon University

Description

We demonstrate high accuracies classifying working memory load using an electroencephalograph (EEG), even with little temporallag, not much training data, and a small number of EEG channels.

Categories

Top: Biology: Neuroscience

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*Slides*
0:00	Feasibility and Pragmatics of Classifying Working Memory Load with an Electroencephalograph
0:00	Motivation
1:24	Background
2:23	Related Work
3:48	Differences across subjects
4:52	Goals and Contribution
8:22	General Methodology (Training)
9:57	General Methodology (Classification)
10:25	Feature Selection and Classification
13:01	Obtaining Training Data
14:15	Feature Selection and Classification (1)
14:16	Feature Selection and Classification (2)
14:19	Feature Selection and Classification (3)
14:20	Experiment: Task
16:12	n-Back stimuli
17:07	Equipment and Participants
18:37	Experiment: Design and Procedure
19:35	Experiment: Behavioral Results
20:21	Results
21:14	Window Size Results
22:40	Amount of Training Data
22:59	Number of EEG Channels
24:25	Cross Task Results
25:18	Recap
26:09	Conclusion and Future Work
27:00	Questions?

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