An efficient word typing P300-BCI system using a modified T9 interface and random forest classifier

Akram, Faraz; Han, Sangheon; Kim, Tae‐Seong

doi:10.1016/j.compbiomed.2014.10.021

Cited by 83 publications

(39 citation statements)

References 22 publications

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“…This is graphically shown in the boxplots of Figure 7(a) where it is seen that the target detection is improved and also the dispersion becomes lower as more trials were scored. This was consistent with the conventional P300 speller knowledge [ 5 , 16 , 59 ], but using a stimulation screen with a snapshot background and no orthographic or semantic stimulation markers.…”

Section: Resultssupporting

confidence: 82%

Scenario Screen: A Dynamic and Context Dependent P300 Stimulator Screen Aimed at Wheelchair Navigation Control

Piña-Ramírez

Valdés-Cristerna

Yáñez-Suárez

2018

Computational and Mathematical Methods in Medicine

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P300 spellers have been widely modified to implement nonspelling tasks. In this work, we propose a “scenario” stimulation screen that is a P300 speller variation to command a wheelchair. Our approach utilized a stimulation screen with an image background (scenario snapshot for a wheelchair) and stimulation markers arranged asymmetrically over relevant landmarks, such as suitable paths, doors, windows, and wall signs. Other scenario stimulation screen features were green/blue stimulation marker color scheme, variable Interstimulus Interval, single marker stimulus mode, and optimized stimulus sequence generator. Eighteen able-bodied subjects participated in the experiment; 78% had no experience in BCI usage. A waveform feature analysis and a Mann–Whitney U test performed over the pairs of target and nontarget coherent averages confirmed that 94% of the subjects elicit P300 (p < .005) on this modified stimulator. Least Absolute Shrinkage and Selection Operator optimization and Linear Discriminant Analysis were utilized for the automatic detection of P300. For evaluation with unseen data, target detection was computed (median sensitivity = 1.00 (0.78–1.00)), together with nontarget discrimination (median specificity = 1.00 (0.98–1.00)). The scenario screen adequately elicits P300 and seems suitable for commanding a wheelchair even when users have no previous experience on the BCI spelling task.

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Section: Resultssupporting

confidence: 82%

Scenario Screen: A Dynamic and Context Dependent P300 Stimulator Screen Aimed at Wheelchair Navigation Control

Piña-Ramírez

Valdés-Cristerna

Yáñez-Suárez

2018

Computational and Mathematical Methods in Medicine

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“…The first time a T9 (text on nine keys) paradigm was presented as a BCI speller, it was based on auditory stimuli [ 101 ]. A modified, visual-based stimuli T9 speller system was introduced in 2015 [ 75 ] and is shown in Figure 5 , with an integrated dictionary to propose suggested words to save time. T9 is the same approach used in early mobile phones for texting on the number keypad.…”

Section: Review Of Bci Spellersmentioning

confidence: 99%

Brain–Computer Interface Spellers: A Review

et al. 2018

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A Brain–Computer Interface (BCI) provides a novel non-muscular communication method via brain signals. A BCI-speller can be considered as one of the first published BCI applications and has opened the gate for many advances in the field. Although many BCI-spellers have been developed during the last few decades, to our knowledge, no reviews have described the different spellers proposed and studied in this vital field. The presented speller systems are categorized according to major BCI paradigms: P300, steady-state visual evoked potential (SSVEP), and motor imagery (MI). Different BCI paradigms require specific electroencephalogram (EEG) signal features and lead to the development of appropriate Graphical User Interfaces (GUIs). The purpose of this review is to consolidate the most successful BCI-spellers published since 2010, while mentioning some other older systems which were built explicitly for spelling purposes. We aim to assist researchers and concerned individuals in the field by illustrating the highlights of different spellers and presenting them in one review. It is almost impossible to carry out an objective comparison between different spellers, as each has its variables, parameters, and conditions. However, the gathered information and the provided taxonomy about different BCI-spellers can be helpful, as it could identify suitable systems for first-hand users, as well as opportunities of development and learning from previous studies for BCI researchers.

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“…Random forests classifier (RFC) is one amongst the foremost productive ensemble learning techniques and performs the classification by averaging the multiple decision trees. RFC proved to be more accurate, and fast as comparison to any other single classifier because of removing the difficulty of over fitting and high variance [13].…”

Section: Discussionmentioning

confidence: 99%

Random Forest Classifier based on Variable Precision Rough Set Theory

Jain¹,

Vishwakarma²,

Jain³

2017

IJCA

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Decision-making process is supported by Machine learningbased classification techniques in many areas of health care. Classification performance of decision system can be improved using the attribute reduction mainly in the situation of high data dimensionality dilemma .This paper proposes, Random forest Classifier (RFC) approach which is based on the Variable Precision Rough Set (VPRS) theory. The first phase of proposed approach focus at attribute reduction of available dataset using VPRS .Directing from dimensionality reduction to predictive model construction, and in next phase, the obtained abridged dataset is provided as the input of RFC to build a more accurate classification model. The performance is evaluated in terms of classification accuracy and time complexity. The experimental results show that the enhanced RFC has higher accuracy and correctly classified instances as compared with the existing algorithms.

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An efficient word typing P300-BCI system using a modified T9 interface and random forest classifier

Cited by 83 publications

References 22 publications

Scenario Screen: A Dynamic and Context Dependent P300 Stimulator Screen Aimed at Wheelchair Navigation Control

Scenario Screen: A Dynamic and Context Dependent P300 Stimulator Screen Aimed at Wheelchair Navigation Control

Brain–Computer Interface Spellers: A Review

Random Forest Classifier based on Variable Precision Rough Set Theory

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