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

Abstract

The conventional P300-based character spelling BCI system consists of a character presentation paradigm and a classification system. In this paper, we propose modifications to both in order to increase the word typing speed and accuracy. In the paradigm part, we have modified the T9 (Text on Nine keys) interface which is similar to the keypad of mobile phones being used for text messaging. Then we have integrated a custom-built dictionary to give word suggestions to a user while typing. The user can select one out of the given suggestions to complete word typing. Our proposed paradigms significantly reduce the word typing time and make words typing more convenient by typing complete words with only few initial character spellings. In the classification part we have adopted a Random Forest (RF) classifier. The RF improves classification accuracy by combining multiple decision trees. We conducted experiments with five subjects using the proposed BCI system. Our results demonstrate that our system increases typing speed significantly: our proposed system took an average time of 1.83 minutes per word, while typing ten random words, whereas the conventional spelling required 3.35 minutes for the same words under the same conditions, decreasing the typing time by 45.37%.

The Human Factors and Ergonomics of P300-Based Brain-Computer Interfaces

“…The age of participants varied by study. Nineteen out of twenty-two articles explicitly disclosed the age information of their subjects; one article only mentioned that the participants were undergraduate students [ 44 ]; two did not mention participants’ age [ 47 , 58 ]. Based on the known information, the oldest participant was 68 years old, while the youngest one was 14.…”

“…Generally, people with disabilities tend to be older and people in the healthy group tend to be younger. Sixteen articles reported the gender distribution of their participants [ 34 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 53 , 54 , 56 , 57 , 59 , 61 ]. The average percentage of male subjects among these papers was 76.4%.…”

“…In addition, three articles mentioned “cognitive disabilities” [ 56 , 58 , 62 ]. Twelve studies reported all healthy subjects [ 43 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 53 , 54 , 55 , 59 ], and one study did not disclose this information [ 61 ].…”

Exploiting EEG Channel Correlations in P300 Speller Paradigm for Brain-Computer Interface

“…It achieves the top performance in BCI competition III for P300 speller paradigm [27]. Besides, random forest classification [20] and convolutional neural network (CNN) [28] have been applied in P300 speller paradigm.…”

“…It can also be used to separate artifacts [17], [18]. Comparison about the blind source separation techniques in P300 speller paradigm is presented in [19], [20]. Common spatial patterns (CSP) [21] is a discriminative processing way, with the goal to maximize the difference in variance.…”