EEG-Based Classification of the Driver Alertness State
نویسندگان
چکیده
منابع مشابه
Fuzzy Rule-based Alertness State Classification Based on the Optimization of Eeg Rhythm/channel Combinations
This paper presents a method for automatically selecting the optimal EEG rhythm/channel combination capable of classifying the different human alertness states. We considered four alertness states, namely ’engaged’, ’calm’, ’drowsy’, and ’asleep’. Energies associated with the conventional EEG rhythms, δ, θ, α, β and γ, extracted from overlapping segments of the different EEG channels were used ...
متن کاملSubtractive Fuzzy Classifier Based Driver Distraction Levels Classification Using EEG
[Purpose] In earlier studies of driver distraction, researchers classified distraction into two levels (not distracted, and distracted). This study classified four levels of distraction (neutral, low, medium, high). [Subjects and Methods] Fifty Asian subjects (n=50, 43 males, 7 females), age range 20-35 years, who were free from any disease, participated in this study. Wireless EEG signals were...
متن کاملDifferent Techniques to Quantify the Driver Alertness
Driving is a daily activity throughout the world. However, driving at highways or city requires significant cognitive and motor skills such as visual-spatial ability, memory and information processing and rapid reaction. Recent studies show lack of driver alertness to be a major cause of automobile accidents on roadways across the world. The perceived state of alertness is a complex physiologic...
متن کاملAL . : ESTIMATING ALERTNESS FORM THE EEG POWER SPECTRUM 1 Estimating Alertness from the EEG
In tasks requiring sustained attention, human alertness varies on a minute time scale. This can have serious consequences in occupations ranging from air traac control to monitoring of nuclear power plants. Changes in the electroencephalographic (EEG) power spectrum accompany these uctuations in the level of alertness, as assessed by measuring simultaneous changes in EEG and performance on an a...
متن کاملClassification of EEG-based motor imagery BCI by using ECOC
AbstractAccuracy in identifying the subjects’ intentions for moving their different limbs from EEG signals is regarded as an important factor in the studies related to BCI. In fact, the complexity of motor-imagination and low amount of signal-to-noise ratio for EEG signal makes this identification as a difficult task. In order to overcome these complexities, many techniques such as variou...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Current Directions in Biomedical Engineering
سال: 2020
ISSN: 2364-5504
DOI: 10.1515/cdbme-2020-3091