2015
DOI: 10.1016/j.compbiomed.2014.10.021
|View full text |Cite
|
Sign up to set email alerts
|

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

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

1
30
0
7

Year Published

2017
2017
2021
2021

Publication Types

Select...
5
5

Relationship

0
10

Authors

Journals

citations
Cited by 82 publications
(38 citation statements)
references
References 22 publications
1
30
0
7
Order By: Relevance
“…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%
“…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%
“…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%
“…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%