Connecting a Consumer Brain-Computer Interface to an Internet-of-Things Ecosystem

Mathe, Eirini; Spyrou, Evaggelos

doi:10.1145/2910674.2935844

Cited by 14 publications

(8 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, BCIs are also used to synthesize speech by people who are unable to communicate due to neurological impairments [ 4 ]. BCIs have also been used to improve rehabilitation after a stroke, translating brain signals into the intended movements of a paralyzed limb [ 5 ] and, more recently, for the control through thought of applications for smart homes or robots within the Internet of Things (IoT) context [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

On the Better Performance of Pianists with Motor Imagery-Based Brain-Computer Interface Systems

Riquelme-Ros

Rodríguez-Bermúdez

Rodríguez‐Rodríguez

et al. 2020

Sensors

View full text Add to dashboard Cite

Motor imagery (MI)-based brain-computer interface (BCI) systems detect electrical brain activity patterns through electroencephalogram (EEG) signals to forecast user intention while performing movement imagination tasks. As the microscopic details of individuals’ brains are directly shaped by their rich experiences, musicians can develop certain neurological characteristics, such as improved brain plasticity, following extensive musical training. Specifically, the advanced bimanual motor coordination that pianists exhibit means that they may interact more effectively with BCI systems than their non-musically trained counterparts; this could lead to personalized BCI strategies according to the users’ previously detected skills. This work assessed the performance of pianists as they interacted with an MI-based BCI system and compared it with that of a control group. The Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA) machine learning algorithms were applied to the EEG signals for feature extraction and classification, respectively. The results revealed that the pianists achieved a higher level of BCI control by means of MI during the final trial (74.69%) compared to the control group (63.13%). The outcome indicates that musical training could enhance the performance of individuals using BCI systems.

show abstract

Section: Introductionmentioning

confidence: 99%

On the Better Performance of Pianists with Motor Imagery-Based Brain-Computer Interface Systems

Riquelme-Ros

Rodríguez-Bermúdez

Rodríguez‐Rodríguez

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Taking this use of EEG one step forward multiple articles [15][16][17][18][19][20][21] discuss integrating EEG with the Internet of Things (IOT) to control different devices in a smart home using protocols like Message Queue Telemetry Transport (MQTT) and Extensible Messaging and Presence Protocol (XMPP). These interfaces can further be used to improve healthcare and the general living standard of differently-abled people and in-turn make everyone's life easier.…”

Section: Review Of Eeg For Bci Applicationsmentioning

confidence: 99%

“…A similar algorithm is applied to control the fan, however in that case, changes in a combination of frequency and amplitude; in the Beta range of frequency (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), meeting the threshold result in the change of state of the fan. Based on further analysis of the EEG signal in various frequency ranges, multiple such IOT devices can be controlled.…”

Section: Algorithm Work-flowmentioning

confidence: 99%

Design of an EEG Acquisition System for Embedded Edge Computing

Rai¹,

Thakur²,

Mane³

et al. 2020

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

The human brain is one of the most complex machines on the planet. Being the only method to get real-time data with high temporal resolution from the brain makes EEG a highly sought upon signal in the neurological and psychiatric domain. However, recent developments in this field have made EEG more than just a tool for medical professionals. The decreasing size and increasing complexity of EEG acquisition systems have brought it out of the lab and into the field where it is used for varied applications like neurofeedback, person recognition and other recreational activities. Amalgamation of the EEG signal with new developing standards of Industry 4.0 to control basic IOT devices using edge computing techniques marks the next step in the design and development our low-cost yet robust Brain Computer Interface (BCI); which is just one of the many applications that a versatile and well-built EEG acquisition system can be used for.

show abstract

“…Brain-to-brain interface systems are a combination of BCI systems and neurostimulation, and EEG-based BCI protocols are among the least invasive and lowest cost to implement (Volosyak et al, 2010;Mathe and Spyrou, 2016). EEG offers neurophysiological recording with high temporal resolution using a proven coordinate system.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Computer–Brain Interface Parameters for Non-invasive Brain-to-Brain Interface

LaRocco¹,

Paeng²

2020

Front. Neuroinform.

View full text Add to dashboard Cite

A non-invasive, brain-to-brain interface (BBI) requires precision neuromodulation and high temporal resolution as well as portability to increase accessibility. A BBI is a combination of the brain-computer interface (BCI) and the computer-brain interface (CBI). The optimization of BCI parameters has been extensively researched, but CBI has not. Parameters taken from the BCI and CBI literature were used to simulate a two-class medical monitoring BBI system under a wide range of conditions. BBI function was assessed using the information transfer rate (ITR), measured in bits per trial and bits per minute. The BBI ITR was a function of classifier accuracy, window update rate, system latency, stimulation failure rate (SFR), and timeout threshold. The BCI parameters, including window length, update rate, and classifier accuracy, were kept constant to investigate the effects of varying the CBI parameters, including system latency, SFR, and timeout threshold. Based on passively monitoring BCI parameters, a base ITR of 1 bit/trial was used. The optimal latency was found to be 100 ms or less, with a threshold no more than twice its value. With the optimal latency and timeout parameters, the system was able to maintain near-maximum efficiency, even with a 25% SFR. When the CBI and BCI parameters are compared, the CBI's system latency and timeout threshold should be reflected in the BCI's update rate. This would maximize the number of trials, even at a high SFR. These findings suggested that a higher number of trials per minute optimizes the ITR of a non-invasive BBI. The delays innate to each BCI protocol and CBI stimulation method must also be accounted for. The high latencies in each are the primary constraints of non-invasive BBI for the foreseeable future.

show abstract

Connecting a Consumer Brain-Computer Interface to an Internet-of-Things Ecosystem

Cited by 14 publications

References 4 publications

On the Better Performance of Pianists with Motor Imagery-Based Brain-Computer Interface Systems

On the Better Performance of Pianists with Motor Imagery-Based Brain-Computer Interface Systems

Design of an EEG Acquisition System for Embedded Edge Computing

Optimizing Computer–Brain Interface Parameters for Non-invasive Brain-to-Brain Interface

Contact Info

Product

Resources

About