scholarly journals Pitfalls in the Assessment of Brain-Machine Interfaces Using Information Transfer Rate

2017 ◽  
Author(s):  
Mikhail A. Lebedev ◽  
Po-He Tseng ◽  
Peter J. Ifft ◽  
Dennis Ochei ◽  
Miguel A.L. Nicolelis
Keyword(s):  
Motor Performance ◽  
Transfer Rate ◽  
Information Transfer ◽  
List Type ◽  
Brain Machine Interfaces ◽  
Practical Applications ◽  
Information Transfer Rate ◽  
Recording Channels ◽  
Transfer Of Information

AbstractInformation transfer rate (ITR), measured in bits/s, can be applied to evaluate motor performance, including the capacity of brain-machine interfaces (BMIs) to control external actuators. In a 2013 article entitled “Transfer of information by BMI” and published in Neuroscience, Tehovnik and his colleagues utilized ITR to assess the performance of several BMIs reported in the literature. We examined these analyses closely and found several fundamental flaws in their evaluation of ITR. Here we discuss the pitfalls in Tehovnik’s measurements of ITR, as well as several other issues raised in “Transfer of information by BMI”, including the claim that BMIs cannot be a reasonable option for paralyzed patients.HighlightsInformation transfer rate is discussed for BMI experiments, where subjects reach to targets.Task settings, not just the number of possible targets, are important to calculate information correctly.Active tactile exploration can be quantified as information transfer, but the number of targets is insufficient for such quantification.Information transfer rate increases with the number of neural recording channels.For practical applications, improvement in quality of life is essential, not information transfer rate per se.

scholarly journals Echo State Networks Ensemble for SSVEP Dynamical Online Detection

2018 ◽  
Author(s):  
D Ibanez-Soria ◽  
A. Soria-Frisch ◽  
J Garcia-Ojalvo ◽  
G Ruffini
Keyword(s):  
Steady State ◽  
Canonical Correlation ◽  
Transfer Rate ◽  
Information Transfer ◽  
Complex Nature ◽  
Detection Accuracy ◽  
List Type ◽  
Maximum Information ◽  
Echo State Networks ◽  
Information Transfer Rate

BackgroundRecent years have witnessed an increased interest in the use of steady state visual evoked potentials (SSVEPs) in brain computer interfaces (BCI), SSVEP is considered a stationary brain process that appears when gazing at a stimulation light source.New MethodsThe complex nature of brain processes advocates for non-linear EEG analysis techniques. In this work we explore the use of an Echo State Networks (ESN) based architecture for dynamical SSVEP detection.ResultsWhen simulating a 6-degrees of freedom BCI system, an information transfer rate of 49bits/min was achieved. Detection accuracy proved to be similar for observation windows ranging from 0.5 to 4 seconds.Comparison with existing methodsSSVEP detection performance has been compared to standard canonical correlation analysis (CCA). CCA achieved a maximum information transfer rate of 21 bits/minute. In this case detection accuracy increased along with the observation window lengthConclusionsAccording to here presented results ESN outperforms standard canonical correlation and has proved to require shorter observation time windows. However ESN and CCA approaches delivered diverse classification accuracies at subject level for various stimulation frequencies, proving to be complementary methods. A possible explanation of these results may be the occurrence of evoked responses of different nature, which are then detected by different approaches. While reservoir computing methods are able to detect complex dynamical patterns and/or complex synchronization among EEG channels, CCA exclusively captures stationary patterns. Therefore, the ESN-based approach may be used to extend the definition of steady-state response, considered so far a stationary process.HighlightsWe present a novel SSVEP dynamical detection approach based on ESN.This is the first time ESNs are applied to SSVEP based BCI systems.We provide experimental validation of proposed methodology.Experimental results indicate non-stationarity in SSVEP patterns.

A Dual Stimuli Approach Combined with Convolutional Neural Network to Improve Information Transfer Rate of Event-Related Potential-Based Brain-Computer Interface

2018 ◽  
Vol 28 (10) ◽  
pp. 1850034 ◽  
Author(s):  
Wei Li ◽  
Mengfan Li ◽  
Huihui Zhou ◽  
Genshe Chen ◽  
Jing Jin ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Rate ◽  
Information Transfer ◽  
Brain Computer Interface ◽  
Event Related Potential ◽  
Computer Interface ◽  
Generation Rate ◽  
Information Transfer Rate ◽  
Command Generation

Increasing command generation rate of an event-related potential-based brain-robot system is challenging, because of limited information transfer rate of a brain-computer interface system. To improve the rate, we propose a dual stimuli approach that is flashing a robot image and is scanning another robot image simultaneously. Two kinds of event-related potentials, N200 and P300 potentials, evoked in this dual stimuli condition are decoded by a convolutional neural network. Compared with the traditional approaches, this proposed approach significantly improves the online information transfer rate from 23.0 or 17.8 to 39.1 bits/min at an accuracy of 91.7%. These results suggest that combining multiple types of stimuli to evoke distinguishable ERPs might be a promising direction to improve the command generation rate in the brain-computer interface.

scholarly journals A low-cost auditory multi-class brain-computer interface based on pitch, spatial and timbre cues

2013 ◽  
Author(s):  
Zacharias Vamvakousis ◽  
Rafael Ramirez
Keyword(s):  
Healthy Subjects ◽  
Transfer Rate ◽  
Information Transfer ◽  
Low Cost ◽  
Auditory Stimuli ◽  
Stimulus Interval ◽  
Inter Stimulus Interval ◽  
Auditory P300 ◽  
Information Transfer Rate ◽  
Cord Injury

P300-based brain-computer interfaces (BCIs) are especially useful for people with illnesses, which prevent them from communicating in a normal way (e.g. brain or spinal cord injury). However, most of the existing P300-based BCI systems use visual stimulation which may not be suitable for patients with sight deterioration (e.g. patients suffering from amyotrophic lateral sclerosis). Moreover, P300-based BCI systems rely on expensive equipment, which greatly limits their use outside the clinical environment. Therefore, we propose a multi-class BCI system based solely on auditory stimuli, which makes use of low-cost EEG technology. We explored different combinations of timbre, pitch and spatial auditory stimuli (TimPiSp: timbre-pitch-spatial, TimSp: timbre-spatial, and Timb: timbre-only) and three inter-stimulus intervals (150ms, 175ms and 300ms), and evaluated our system by conducting an oddball task on 7 healthy subjects. This is the first study in which these 3 auditory cues are compared. After averaging several repetitions in the 175ms inter-stimulus interval, we obtained average selection accuracies of 97.14%, 91.43%, and 88.57% for modalities TimPiSp, TimSp, and Timb, respectively. Best subject’s accuracy was 100% in all modalities and inter-stimulus intervals. Average information transfer rate for the 150ms inter-stimulus interval in the TimPiSp modality was 14.85 bits/min. Best subject’s information transfer rate was 39.96 bits/min for 175ms Timbre condition. Based on the TimPiSp modality, an auditory P300 speller was implemented and evaluated by asking users to type a 12-characters-long phrase. Six out of 7 users completed the task. The average spelling speed was 0.56 chars/min and best subject’s performance was 0.84 chars/min. The obtained results show that the proposed auditory BCI is successful with healthy subjects and may constitute the basis for future implementations of more practical and affordable auditory P300-based BCI systems.

SSVEP-P300 HYBRID PARADIGM OPTIMIZATION FOR ENHANCED INFORMATION TRANSFER RATE

2020 ◽  
Vol 32 (01) ◽  
pp. 2050003
Author(s):  
Akshay Katyal ◽  
Rajesh Singla
Keyword(s):  
Transfer Rate ◽  
Information Transfer ◽  
Brain Response ◽  
Information Transfer Rate ◽  
User Intentions ◽  
Activation Rate ◽  
Optimal Protocol ◽  
Brain Computer Interfacing ◽  
Average Information ◽  
Two Hybrid

Hybrid brain–computer interfacing (BCI), recently, has been the epicenter of research in the area of rehabilitation engineering. The concept is based on the principle that the paradigm used for the BCI elicits one BCI marker in combination with one or more BCI modalities or other physiological signals. These paradigms elicit human brain response to successfully determine user intentions. Steady-state visually evoked potential (SSVEP) has been the favourite amongst researchers to combine with other BCI modalities such as P300, Motor Imagery (MI), etc. to develop assistive devices (ADs) based on hybrid BCI. This research paper is a record of a comparative study conducted between two hybrid BCI’s, namely hybrid BCI-1, hybrid BCI-2 and traditional SSVEP BCI. Both hybrid paradigms are similar in schematics but differ in the operational protocol. The study aimed to find the optimal protocol which greatly enhances the average information transfer rate (ITR) of a BCI-based AD. Hybrid BCI-1 showed lower classification accuracy (90.36%) and higher false activation rate (FAR) (3.16%) as compared to Hybrid BCI-2 (92.35% and 2.78%, respectively) as well as traditional SSVEP (93.38% and 2.73%, respectively). However, the average ITR of Hybrid BCI-1 (80.76 bits/min) was much higher than that of Hybrid BCI-2 (41.21 bits/min) and traditional SSVEP paradigm (36.34 bits/min). This led to the conclusion, that Hybrid BCI-1 is the most viable option for developing an AD.

SSVEP Recognition by Using Higher Harmonics Based on Music

2016 ◽  
Vol 16 (4) ◽  
Author(s):  
Kun Chen ◽  
Fei Xu ◽  
Quan Liu ◽  
Haojie Liu ◽  
Yang Zhang ◽  
...  
Keyword(s):  
Transfer Rate ◽  
Information Transfer ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Signal To Noise Ratio ◽  
Signal Classification ◽  
Recognition Time ◽  
Multiple Signal Classification ◽  
Computer Interfaces ◽  
Information Transfer Rate

Among different brain–computer interfaces (BCIs), the steady-state visual evoked potential (SSVEP)-based BCI has been widely used because of its higher signal to noise ratio (SNR) and greater information transfer rate (ITR). In this paper, a method based on multiple signal classification (MUSIC) was proposed for multidimensional SSVEP signal processing. Both fundamental and second harmonics of SSVEPs were employed for the final target recognition. The experimental results proved it has the advantage of reducing recognition time. Also, the relation between the duty-cycle of the stimulus signals and the amplitude of the second harmonics of SSVEPs was discussed via experiments. In order to verify the feasibility of proposed methods, a two-layer spelling system was designed. Different subjects including those who have never used BCIs before used the system fluently in an unshielded environment.

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