Using a Low-Cost Electroencephalograph for Task Classification in HCI Research

Using a Low-Cost Electroencephalograph for Task Classification in HCI Research

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Using a Low-Cost Electroencephalograph for Task Classification in HCI Research Johnny Chung Lee Carnegie Mellon University 5000 Forbes Ave, Pittsburgh, PA 15213 johnny@cs.cmu.edu Desney S. Tan Microsoft Research One Microsoft Way, Redmond, WA 98052 desney@microsoft.com ABSTRACT Modern brain sensing technologies provide a variety of methods for detecting specific forms of brain activity. In this paper, we present an initial step in exploring how these technologies may be used to perform task classification and applied in a relevant manner to HCI research. We describe two experiments showing successful classification between tasks using a low-cost off-the-shelf electroencephalograph (EEG) system. In the first study, we achieved a mean classi- fication accuracy of 84.0% in subjects performing one of three cognitive tasks - rest, mental arithmetic, and mental rotation - while sitting in a controlled posture. In the second study, conducted in more ecologically valid setting for HCI research, we attained a mean classification accuracy of 92.4% using three tasks that included non-cognitive fea- tures: a relaxation task, playing a PC based game without opponents, and engaging opponents within the game. Throughout the paper, we provide lessons learned and dis- cuss how HCI researchers may utilize these technologies in their work. Categories and Subject Descriptors : H.1.2 [User/Machine Sys- tems]; H.5.2 [User Interfaces]: Input devices and strategies; B.4.2 [Input/Output Devices]: Channels and controllers; J.3 [Life and Medical Sciences]. General Terms : Human Factors, Experimentation. Keywords : Brain-Computer Interface, human cognition, physical artifacts, task classification, Electroencephalogram (EEG). INTRODUCTION For generations, humans have fantasized about the ability to communicate and interact with machines through thought alone or to create devices that can peer into a person’s thoughts. These ideas have captured the imagination of hu- mankind in the form of ancient myths and modern science fiction stories. However, only in recent decades have ad- vances in neuroscience and brain sensing technologies made measurable progress toward achieving that vision. These technologies allow us to monitor the physical proc- esses within the brain that correspond with certain forms of thought. Primarily driven by growing societal recognition for the needs of people with physical disabilities, researchers have used these technologies to build brain-computer interfaces (BCIs), communication systems that do not depend on the brain’s normal output pathways of peripheral nerves and muscles [17]. A conceptual illustration of a BCI system is shown in Figure 1. In these systems, users explicitly ma- nipulate their brain activity instead of using motor move- ments to produce signals that can be used to control com- puters or communication devices. The impact of this work is extremely high, especially to those who suffer from dev- astating neurodegenerative diseases such as amyotrophic
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This note was uploaded on 04/08/2010 for the course CS 420 taught by Professor Dawsonengler during the Spring '02 term at San Jose State University .

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Using a Low-Cost Electroencephalograph for Task Classification in HCI Research

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