scholarly journals Evaluation of a modified Fitts law brain–computer interface target acquisition task in able and motor disabled individuals

2009 ◽  
Vol 6 (5) ◽  
pp. 056002 ◽  
Author(s):  
E A Felton ◽  
R G Radwin ◽  
J A Wilson ◽  
J C Williams
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Target Acquisition ◽  
Fitts Law ◽  
Acquisition Task

Development of a Control Theoretic Alternative to Fitts’ Law With Application to In-Vehicle Touchscreen Interface

2015 ◽  
Author(s):  
Chao Wang ◽  
Bahram Ravani
Keyword(s):  
Physical Interpretation ◽  
Model Identification ◽  
Human Operator ◽  
Target Acquisition ◽  
Relative Movement ◽  
Brief Treatment ◽  
Fitts Law ◽  
Proposed Model ◽  
Acquisition Task

A control theoretic alternative to Fitts’ Law based on a modified crossover model is proposed. A physical interpretation of the model offers the unification of various different formulations of Fitts’ Law. Model identification and validation is carried out for a target acquisition task using a touchscreen. A brief treatment of noise generated by random relative movement between the human operator and the touchscreen (such as in-vehicle touchscreen based devices) is considered using the proposed model.

Comparison of Six Cursor Control Devices Based on Fitts' Law Models

1986 ◽  
Vol 30 (4) ◽  
pp. 327-331 ◽  
Author(s):  
Brian W. Epps
Keyword(s):  
Regression Analysis ◽  
Movement Time ◽  
Performance Data ◽  
Target Acquisition ◽  
Acquisition Performance ◽  
Cursor Control ◽  
Fitts Law ◽  
Acquisition Task ◽  
Control Devices ◽  
Best Fit

Six cursor control devices were compared on a target acquisition task which required subjects to move a cursor into square targets of varying sizes and at various screen distances. The target acquisition performance data were fitted to movement time models proposed by Fitts, Jagacinski, and Kvalseth. Regression analysis results indicated good predictions of target acquisition performance for the six cursor devices. The best fit was obtained with the trackball across the three models.

Simultaneous Classification of Multiple Motor Imagery and P300 for Increase in Output Information of Brain-computer Interface

2013 ◽  
Vol 133 (3) ◽  
pp. 635-641
Author(s):  
Genzo Naito ◽  
Lui Yoshida ◽  
Takashi Numata ◽  
Yutaro Ogawa ◽  
Kiyoshi Kotani ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Output Information

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

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