Electroencephalographic (EEG) recordings are, most of
the times, corrupted by spurious artifacts, which should be
rejected or cleaned by the practitioner. As human scalp EEG
screening is error-prone, automatic artifact detection is an issue
of capital importance, to ensure objective and reliable results.
In this paper we ...»»»»
Electroencephalographic (EEG) recordings are, most of
the times, corrupted by spurious artifacts, which should be
rejected or cleaned by the practitioner. As human scalp EEG
screening is error-prone, automatic artifact detection is an issue
of capital importance, to ensure objective and reliable results.
In this paper we propose a new approach for discrimination
of muscular activity in the human scalp quantitative
EEG (QEEG), based on the time-frequency shape analysis.
The impact of the muscular activity on the EEG can be evaluated
from this methodology. We present an application of
this scoring as a preprocessing step for EEG signal analysis,
in order to evaluate the amount of muscular activity for two
set of EEG recordings for dementia patients with early stage
of Alzheimer’s disease and control age-matched subjects.^^^^
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