This paper proposes a novel and simple local neural classifier for the recognition of mental tasks from on-line spontaneous EEG signals. The proposed neural classifier recognizes three mental tasks from on-line spontaneous EEG signals. Correct recognition is around 70%. This modest rate is largely compensated by two properties, namely low percentage of wrong decisions (below 5%) and rapid responses (every 1/2 s). Interestingly, the neural classifier achieves this performance with a few units, normally just one per mental task. Also, since the subject and his/her personal Interface learn simultaneously from each other, subjects master it rapidly (in a few days of moderate training). Finally, analysis of learned EEG patterns confirms that for...
2017 International Artificial Intelligence and Data Processing Symposium, IDAP 2017 -- 16 September ...
Electroencephalogram (EEG) signals represent an important class of biological signals whose behavior...
In this article we describe a new method for supervised classification of EEG signals. This method a...
This paper proposes a new local neural classifier for the recognition of mental tasks from on-line s...
This paper investigates appropriate neural classifiers for the recognition of mental tasks from on-l...
There is a growing interest in the use of physiological signals for communication and operation of d...
Abstract — BCI (Brain Computer Interface) is the method of communication between neural activity of ...
Abstract: This paper proposes and creates a new generalised view towards BCI with its related applic...
BACKGROUND. Mental task identification using electroencephalography (EEG) signals is required for pa...
Abstract Classification of different mental tasks using electroencephalogram (EEG) signal plays an i...
Abstract not availableJRC.G-Institute for the Protection and the Security of the Citizen (Ispra
This paper presents the classification of three mental tasks, using the EEG signal and simulating a ...
Electroencephalogram, or EEG, signals are an important source of information for the study of underl...
The aim of this paper is to propose a real-time classification algorithm for the low-amplitude elect...
The advancement in computer hardware and digital signal processing made possible use of brain signal...
2017 International Artificial Intelligence and Data Processing Symposium, IDAP 2017 -- 16 September ...
Electroencephalogram (EEG) signals represent an important class of biological signals whose behavior...
In this article we describe a new method for supervised classification of EEG signals. This method a...
This paper proposes a new local neural classifier for the recognition of mental tasks from on-line s...
This paper investigates appropriate neural classifiers for the recognition of mental tasks from on-l...
There is a growing interest in the use of physiological signals for communication and operation of d...
Abstract — BCI (Brain Computer Interface) is the method of communication between neural activity of ...
Abstract: This paper proposes and creates a new generalised view towards BCI with its related applic...
BACKGROUND. Mental task identification using electroencephalography (EEG) signals is required for pa...
Abstract Classification of different mental tasks using electroencephalogram (EEG) signal plays an i...
Abstract not availableJRC.G-Institute for the Protection and the Security of the Citizen (Ispra
This paper presents the classification of three mental tasks, using the EEG signal and simulating a ...
Electroencephalogram, or EEG, signals are an important source of information for the study of underl...
The aim of this paper is to propose a real-time classification algorithm for the low-amplitude elect...
The advancement in computer hardware and digital signal processing made possible use of brain signal...
2017 International Artificial Intelligence and Data Processing Symposium, IDAP 2017 -- 16 September ...
Electroencephalogram (EEG) signals represent an important class of biological signals whose behavior...
In this article we describe a new method for supervised classification of EEG signals. This method a...