An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots. More specifically, motor imagery EEG (MI-EEG), which reflects a subject’s active intent, is attracting increasing attention for a variety of BCI applications....

We investigated how well repetitive finger tapping movements can be decoded from scalp electroencephalography (EEG) signals. A linear decoder with memory was used to infer continuous index finger angular velocities from the low-pass filtered fluctuations of the amplitude of a plurality of EEG signals distributed across the scalp. To evaluate the accuracy of the decoder, the Pearson's correlatio...

The disease epilepsy is characterized by a sudden and recurrent malfunction of the brain that is termed seizer. The electroencephalogram (EEG) signals play an important role in the diagnosis of epilepsy. Nonlinear analysis quantifies the EEG signal to address randomness and predictability of brain activity. In this study, the wavelet subband decomposition and Approximate Entropy (ApEn) is used ...

This paper investigates the feasibility of using neural network (NN) and late gamma band (LGB) electroencephalogram (EEG) features extracted from the brain to identify the individuality of subjects. The EEG signals were recorded using 61 active electrodes located on the scalp while the subjects perceived a single picture. LGB EEG signals occur with jittering latency of above 280 ms and are not ...

In this paper, a new method for automatic sleep stage classification based on time-frequency image (TFI) of electroencephalogram (EEG) signals is proposed. Automatic classification of sleep stages is an important part for diagnosis and treatment of sleep disorders. The smoothed pseudo Wigner-Ville distribution (SPWVD) based time-frequency representation (TFR) of EEG signal has been used to obta...

Wavelets and wavelet transforms (WT) could be a very useful tool to analyze electroencephalogram (EEG) signals. To illustrate the WT method we make use of a simple electric circuit model introduced by Niederhauser [1], which is used to produce EEG-like signals, particularly during an epileptic seizure. The original model is modified to resemble the 10-20 derivation of the EEG measurements. WT i...

background: noise pollution is one of the most harmful ambiance disturbances. it may cause many deficits in ability and activity of persons in the urban and industrial areas. it also may cause many kinds of psychopathies. therefore, it is very important to measure the risk of this pollution in different area. methods: this study was conducted in the department of medical physics and biomedical ...

Electrocorticographic (ECoG) signals represent cortical electrical dipoles generated by synchronous local field potentials that result from simultaneous firing of neurons at distinct frequencies (brain waves). Since different brain waves correlate to different behavioral states, ECoG signals presents a novel strategy to detect complex behaviors. We developed a program, EEG Detection Analysis fo...