Abstract
In this paper, we study the use of EEG (Electroencephalography) to classify between concentrated and relaxed mental states. In the literature, most EEG recording systems are expensive, medical-graded devices. The expensive devices limit the availability in a consumer market. The EEG signals are obtained from a toy-grade EEG device with one channel of output data. The experiments are conducted in two runs, with 7 and 10 subjects, respectively. Each subject is asked to silently recite a five-digit number backwards given by the tester. The recorded EEG signals are converted to time-frequency representations by the software accompanying the device. A simple average is used to aggregate multiple spectral components into EEG bands, such as α, β, and γ bands. The chosen classifiers are SVM (support vector machine) and multi-layer feedforward network trained individually for each subject. Experimental results show that features, with α+β+γ bands and bandwidth 4 Hz, the average accuracy over all subjects in both runs can reach more than 80% and some subjects up to 90+% with the SVM classifier. The results suggest that a brain machine interface could be implemented based on the mental states of the user even with the use of a cheap EEG device.
Highlights
Inputting information to a machine solely based on “thoughts” of the brain is a longseeking goal to communicate with machines
Detecting a person’s mental states by classifying an EEG signal has been studied for many years, many existing papers reported the results based on instrumental EEG devices, which are capable of recording many channels [1,3,4,5,6]
The concentration mental state is invoked by silently reciting a five-digit number backwards
Summary
Inputting information to a machine solely based on “thoughts” of the brain is a longseeking goal to communicate with machines. Detecting a person’s mental states by classifying an EEG signal has been studied for many years, many existing papers reported the results based on instrumental EEG (recording) devices, which are capable of recording many channels [1,3,4,5,6]. To invoke two mental states for classification, we initially recorded EEG for subjects listening to favorite or dislike songs. As pointed out by Kahneman in [16], when a person is doing effortful work, their pupils dilate substantially With this kind of physiological evidence, we think that it could be easier to classify the metal states as effortful or effortless states with a low-cost EEG device. Present a reliable method to invoke two mental states for a low-cost EEG device at a short period of time.
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