Recognition of specific oscillatory patterns in human electroencephalograms (EEGs) is an important problem that has attracted significant attention for creating brain-computer interfaces (BCIs). Some of these patterns are easily identified by various numerical methods. However, it is much more difficult to recognize mental intentions that can be further transformed into control commands for hardware, and the choice of the appropriate numerical tool becomes very important. In this study, we compare several numerical methods applied to multichannel EEGs recorded in untrained volunteers who imagined arm and leg movements. We show that the quality of recognition varies between different methods and depends on the subject. We discuss the possibilities of reliable separation between imaginary movements of various types.
We discuss the ability to recognize the electrical activity of the brain associated with the movements of the hands/legs and imagination of such movements. Conducting experiments with a group of untrained volunteers, we show that real and imaginary movements are clearly detected using the scaling exponent of the detrended fluctuation analysis for the majority of EEG channels (usually 28-31 out of 33). Although this ability is shown regardless of the type of movements, the case of leg movements provided a slightly higher recognition results. This conclusion is supported by numerical estimations based on two quantitative measures.
The ability to recognize certain oscillatory patterns in human EEGs associated with various types of movements is studied on the basis of multiresolution analysis, which uses discrete wavelet-transform with Daubechies functions. It is shown that the dispersion of wavelet-coefficients at distinct levels of resolution enables to distinguish the background electrical activity of the brain, the movement of the arms/legs and the imaginations of different types of movements. The advantage of using wavelets with larger support for improving the quality of recognition is discussed.
Authentic recognition of specific patterns of electroencephalograms (EEGs) associated with real and imagi- nary movements is an important stage for the development of brain-computer interfaces. In experiments with untrained participants, the ability to detect the motor-related brain activity based on the multichannel EEG processing is demonstrated. Using the detrended fluctuation analysis, changes in the EEG patterns during the imagination of hand movements are reported. It is discussed how the ability to recognize brain activity related to motor executions depends on the electrode position.
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