A study of the existing problems of estimating the information transfer rate in online brain–computer interfaces

Yuan, Peng; Gao, Xiaorong; Allison, Brendan Z.; Wang, Yijun; Bin, Guangyu; Gao, Shangkai

doi:10.1088/1741-2560/10/2/026014

Cited by 140 publications

(95 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To evaluate the online performance of the BCI system, we calculated ITR as well as the classification accuracy. The ITR defined by Wolpaw et al [1,38] was calculated via [39,40] ITR ¼ 60 T log 2 N þ p log 2 p þ ð1 À pÞ log 2 1 À p N À 1 !…”

Section: Visual Stimulationmentioning

confidence: 99%

A high-ITR SSVEP-based BCI speller

Chen

Gao

et al. 2014

Brain-Computer Interfaces

Self Cite

240

196

View full text Add to dashboard Cite

Spelling is an important application of brain-computer interfaces (BCIs). Previous BCI spellers were not suited for widespread use due to their low information transfer rate (ITR). In this study, we constructed a high-ITR BCI speller based on the steady-state visual evoked potential (SSVEP). A 45-target BCI speller was implemented with a frequency resolution of 0.2 Hz. A sampled sinusoidal stimulation method was used to present visual stimuli on a conventional LCD screen. The online results revealed that the proposed BCI speller had a good performance, reaching a high average accuracy (84.1% for 2 s stimulation time; 90.2% for 3 s stimulation time) and the corresponding high ITR (105 bits/min for 2 s stimulation time, 82 bits/min for 3 s stimulation time) during the low-frequency stimuli, while 88.7% and 61 bits/ min were achieved for a 4 s time window during the high-frequency stimuli.

show abstract

Section: Visual Stimulationmentioning

confidence: 99%

A high-ITR SSVEP-based BCI speller

Chen

Gao

et al. 2014

Brain-Computer Interfaces

Self Cite

240

196

View full text Add to dashboard Cite

show abstract

“…Information transfer rate (Obermaier et al 2001;Yuan et al 2013) is the bit-rate of the system measured in bits/ symbols as given by Eq. (1) where N is the number of choices which is 4 for the four different shapes and P is the normalized accuracy for classifying tactile data using NB classifier trained offline.…”

Section: Information Transfer Ratementioning

confidence: 99%

Vibrotactile feedback for conveying object shape information as perceived by artificial sensing of robotic arm

et al. 2016

View full text Add to dashboard Cite

This work is a preliminary study towards developing an alternative communication channel for conveying shape information to aid in recognition of items when tactile perception is hindered. Tactile data, acquired during object exploration by sensor fitted robot arm, are processed to recognize four basic geometric shapes. Patterns representing each shape, classified from tactile data, are generated using micro-controller-driven vibration motors which vibrotactually stimulate users to convey the particular shape information. These motors are attached on the subject's arm and their psychological (verbal) responses are recorded to assess the competence of the system to convey shape information to the user in form of vibrotactile stimulations. Object shapes are classified from tactile data with an average accuracy of 95.21 %. Three successive sessions of shape recognition from vibrotactile pattern depicted learning of the stimulus from subjects' psychological response which increased from 75 to 95 %. This observation substantiates the learning of vibrotactile stimulation in user over the sessions which in turn increase the system efficacy. The tactile sensing module and vibrotactile pattern generating module are integrated to complete the system whose operation is analysed in real-time. Thus, the work demonstrates a successful implementation of the complete schema of artificial tactile sensing system for object-shape recognition through vibrotactile stimulations.

show abstract

“…ITR is given by (10) where B represents ITR, N represents the number of possible choices which is 2 at each level of classification in our work and P represents the classification accuracy between 0 and 1. In BCI, ITR is measured in bits/min [37] which is calculated using (11) where T is the time needed to convey each action in second/symbol i.e. time interval from the issue of a command to the generation of the classified output of the same.…”

Section: Online Testing Statisticsmentioning

confidence: 99%

An interval type-2 fuzzy approach for real-time EEG-based control of wrist and finger movement

Bhattacharyya

Pal

Konar

et al. 2015

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

a b s t r a c tFeature extraction and automatic classification of mental states is an interesting and open area of research in the field of brain-computer interfacing (BCI). A well-trained classifier would allow the BCI system to control an external assistive device in real world problems. Sometimes, standard existing classifiers fail to generalize the components of a non-stationary signal, like Electroencephalography (EEG) which may pose one or more problems during real-time usage of the BCI system. In this paper, we aim to tackle this issue by designing an interval type-2 fuzzy classifier which deals with the uncertainties of the EEG signal over various sessions. Our designed classifier is used to decode various movements concerning the wrist (extension and flexion) and finger (opening and closing of a fist). For this purpose, we have employed extreme energy ratio (EER) to construct the feature vector. The average classification accuracy achieved during offline training and online testing over eight subjects are 86.45% and 78.44%, respectively. On comparison with other related works, it is shown that our designed IT2FS classifier presents a better performance.

show abstract

A study of the existing problems of estimating the information transfer rate in online brain–computer interfaces

Cited by 140 publications

References 45 publications

A high-ITR SSVEP-based BCI speller

A high-ITR SSVEP-based BCI speller

Vibrotactile feedback for conveying object shape information as perceived by artificial sensing of robotic arm

An interval type-2 fuzzy approach for real-time EEG-based control of wrist and finger movement

Contact Info

Product

Resources

About