Implementation of a Motor Imagery based BCI System using Python

Programming Language

Luz María Alonso-Valerdi

and Francisco Sepulveda

Department of Research, Tecnológico de Monterrey, Calle del Puente 222,

Col. Ejidos de Huipulco, Tlalpan, 14380, Mexico City, Mexico

School of Computer Science and Electronic Engineering, University of Essex,

Wivenhoe Park, CO4 3SQ, Colchester, Essex, U.K.

Keywords: Brain-computer Interface, Motor Imagery, Python Programming Language.

Abstract: At present, there is a wide variety of free open-source brain-computer interface (BCI) software. Even

though the available software is very complete, it often runs under a Matlab environment. Matlab is a high

performance language for scientific computing, but its limitations concerning the license cost, the restricted

access to the algorithm code, and the portability difficulties complicates its use. Therefore, we proposed to

implement a motor imagery (MI) based BCI system using Python programming language. This system was

called miBCI software, was designed to discriminate up to three control tasks and was structured on the

basis of online and offline data analyses. The functionality and efficiency of the software were firstly

assessed in a pilot study, and then, its applicability and utility were demonstrated in two subsequent studies

associated with the external and internal influences on MI-related control tasks. Results of the pilot study

and preliminary outcomes of the subsequent studies are herein presented. This work contributes by

promoting the utilization of tools which facilitate the advance of BCI research. The advantage of using

Python instead of Matlab, which is the widely used programming language at the moment, is the

opportunity to develop BCI software in a public and collaborative way, without property license restrictions.

1 INTRODUCTION

A brain computer interface (BCI) is a system that

allows individuals to interact with their environment

by translating their brain signals into control

commands for a specific-purpose device. In a typical

non-invasive BCI system, the brain signals are

recorded via electroencephalography (EEG) and

users can modulate their brain signals through

control tasks. Those control tasks are generally

grouped into two types: endogenous or exogenous.

Endogenous control tasks are voluntary mental tasks

that generate distinguishable EEG patterns over the

scalp. Exogenous control tasks direct the user

attention to specific sensory-cognitive stimuli, which

causes automatic and detectable changes in the EEG

signals (Mason and Birch, 2003). The scope of the

present project is limited to BCI systems based on

endogenous control tasks, specifically motor

imagery (MI).

The operation of a BCI system is based on three

functions: (1) data collection from an EEG recorder,

(2) online EEG signal translation, and (3) delivery of

user feedback (Figure 1.1). Frequently, it is also

necessary to store the EEG information to study in

depth the brain signals’ patterns emerged during the

brain-computer communication (Delorme et al.,

2010). The implementation of a BCI system, along

with application software for analysing offline EEG

information, is herein called BCI working

environment.

As BCI research has been growing rapidly in the

last years, the necessity to implement appropriate

BCI working environments where researchers can

conduct their specific-purpose studies has also

arisen. To date, there is a wide variety of computer

programs with BCI applications. The best known

and extensively used BCI software includes

BCI2000 (Mellinger and Schalk, 2007), BCILAB

(Kothe and Makeig, 2013), BioSig (Schlögl et al.,

2007), OpenViBE (Renard et al., 2010), and

EEGLAB (Delorme and Makeig, 2004). Most of the

BCI software has been written in C/C++ or Matlab,

and is free open-source. Even though the previous

Alonso-Valerdi L. and Sepulveda F..

Implementation of a Motor Imagery based BCI System using Python Programming Language.

DOI: 10.5220/0005211500350043

In Proceedings of the 2nd International Conference on Physiological Computing Systems (PhyCS-2015), pages 35-43

ISBN: 978-989-758-085-7

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

Figure 1: General structure of a non-invasive BCI system. Typically, this system first collects the user brain signals from an

EEG recorder. Then, it translates the EEG signals into control commands via a digital signal processor, a feature generator,

and a feature translator. Finally, it provides feedback about the user performance via a control display and translates the

control commands into semantic control signals.

BCI software is very complete, it usually requires

programming skills (such as BCI2000) or runs under

a Matlab environment (such as BCILAB). These

facts could hinder the research progress due to time

consuming or budget restrictions.

With respect to Matlab, this is currently the most

widely used tool for applying computational

methods. In addition, The MathWorks Company

offers other valuable tools such as Simulink for

developing, testing and prototyping BCI approaches

(Ishak, 2009). However, Matlab has three main

limitations, which are: (1) cost, (2) divulgation and

(3) portability (Klein and Reilink, 2013). Firstly and

although Matlab is feasibly afforded by the business

sector, it may become a financial burden for the

private one. For instance, worldwide universities

often purchase a limited number of licenses to deal

with the cost; however, the number of available

licenses seldom satisfies the demand. Furthermore,

The MathWorks Company usually suggests paying

for a low-cost student edition in restricted budget

cases, but only a few of toolboxes are included.

Unfortunately, toolboxes are the most exploitable

resource of Matlab. Secondly, Matlab algorithms are

under a proprietary licensing model. This prohibits

access to the code, which is inconsistent with the

research goals of transparency and reproducibility

(Perez et al., 2011). Finally, the only way to run a

compiled application in Matlab is using the

Component Runtime, but the version of both the

application and the component must be exactly the

same. If we consider that The MathWorks Company

releases a new version every six months, portability

in Matlab becomes completely unfeasible (Klein and

Reilink, 2013).

In the light of the above discussion, several

scientific fields have been gradually turning to other

programming languages, which could offer all the

benefits of Matlab, but under a free open-source

environment. Over the past few years, Python has

become a potential replacement of Matlab because it

provides a comprehensive ecosystem (Perez et al.,

2011). As Matlab, Python has a large variety of

packages for efficient scientific computing. Unlike

Matlab, Python is not limited to the scientific field.

Python is widely used for more general applications

such as web development and database management

(Spacek and Swindale, 2009; Oliphant, 2007;

Lindstrom, 2005). Furthermore, Chavez et al.,

(2006) evaluated the usability, productivity,

performance and scalability aspects of Python on

high performance computing modernization

programs. They proved that Python was powerful

enough to efficiently implement complex

signal/image processing algorithms (Center, 2006).

Python is additionally very accessible to those who

are not programmers. In fact, Fangohr (2004)

compared the programming languages C, Matlab

and Python as teaching languages for engineering

students, and Python was found to be the best choice

in terms of clarity and functionality.

Owing to the limitations of using Matlab and as

Python provides the sufficient tools for

implementing a customised BCI working

environment, the aim of this project was to develop

a MI-based BCI using Python. This was called

miBCI software and a prototype version was

presented in (Alonso-Valerdi and Sepulveda, 2011;

Alonso-Valerdi and Sepulveda, 2011). In this paper,

the final version of the miBCI software is introduced

as follows. First, the development, evaluation and

application of the system are described in detail.

Thereafter, the results obtained from all of the

conducted experimental studies are reported. Finally,

some highlights and future directions are discussed.

PhyCS2015-2ndInternationalConferenceonPhysiologicalComputingSystems

2 METHODS

MI has been extensively employed as control task

because this allows a natural and intuitive BCI

control. Moreover, MI-based BCIs provide the

flexibility to develop autonomous (or asynchronous)

systems. There is a large volume of published

studies describing the use of MI in BCIs. In this

regard, the most prominent work has been reported

by Graz BCI Lab (Pfurtcheller et al., 2007). At

present, this research group has a well-established

procedure to develop synchronous and asynchronous

systems (Pfurtscheller et al., 2011; Leeb et al.,

2007). Such procedure has been illustrated in Figure

2.1 and the architecture of the miBCI software was

founded on those stages related to the development

of a synchronous system.

2.1 Design Considerations

The miBCI software was designed to discriminate up

to three control tasks and structured on the basis of

both online and offline data analyses. Considering

the Graz paradigm for developing synchronous MI-

based BCIs (Figure 2.1), the online data analysis

comprises a cycle of two phases: adaptation and

application. The adaptation phase is a series of

computing processes, which adjusts a mathematical

model (classifier) to a particular EEG dataset. Those

datasets come from training sessions with or without

feedback. The application phase is the utilization of

the model adjusted in the adaptation phase to predict

the user control tasks during training sessions with

feedback or during BCI control. The offline data

analysis comprises digital signal processing (DSP),

feature generation and classification, and plotting.

2.2 Description of the System

The miBCI software was completely written in

Python. It was built on top of Numpy and Scipy

(Oliphant, 2007), in addition to a very complete

plotting library, matplotlib (Hunter, 2007). The

graphical user interface of the software was

programmed on PyGTK , a rich binding for creating

interfaces; the machine learning was supported by

mlpy, a module for (un)supervised problems

(Albanese et al., 2012); and the classifier generation

was provided by LIBSVM, a library for supported

vector classification (Chang and Chih-Jen, 2011).

The miBCI software was created to carry out the

same operations through online and offline analyses,

except for the controlling device operation of the

online process. On this basis, the fundamental

structure of the miBCI software was divided into six

modules: (1) data acquisition, (2) DSP, (3) feature

extraction, (4) feature selection, (5) feature

classification, and (6) plotting tools.

Figure 2.1: Flowchart of the general Graz procedure to

develop MI-based BCIs. Both types of systems

synchronous () and asynchronous (,) are illustrated.

2.2.1 Data Acquisition

At Essex BCI group, user brain signals are recorded

via BIOSEMI equipment. Such company provides

an EEG recording system (ActiveTwo), along with

application software (ActiView). The ActiveTwo

was configured to record the EEG signals within a

400Hz-bandwidth at 2048S/s. The ActiView

software displays the EEG signals, saves EEG data

as BDF-file, and provides a server for network-

oriented communication (TCP/IP).

In the miBCI software, EEG data must be

provided in mat-format organized in three

dimensions (channels, trials, and samples) for offline

analysis. As data are saved as BDF-file, those must

be first converted into mat-files by using the BioSig

library (Schlogl and Brunner, 2008). For online

analysis, a TCP/IP client is used to acquire the

upcoming EEG signals. Refer to Figure 2.2a.

2.2.2 Digital Signal Processing

In essence, the DSP of the miBCI software consists

of spectral and spatial filtering. The spectral method

was included in three ways: (1) ‘50Hz-rejection’,

filtering based on a second-order Butterworth band-

stop filter; (2) ‘Low frequency filtering’, filtering for

removing frequencies below 0.01Hz; and (3)

‘Bandwidth selection’, filtering based on a fourth-

order Butterworth high-pass filter followed by a

seventh-order Butterworth low-pass filter. The

spatial technique was mainly encompassed under

three categories: bipolar, Laplacian, and common

average reference (CAR). Bipolar filtering calculates

a local voltage gradient where the influences of

ImplementationofaMotorImagerybasedBCISystemusingPythonProgrammingLanguage

distant sources are attenuated. Laplacian filtering is

estimated by subtracting the average of surrounding

electrodes from each individual electrode. CAR is

acquired by removing the mean of all the electrodes

from each individual electrode. See Figure 2.2b.

2.2.3 Feature Extraction

Sensory stimulation, cognitive activities, and motor

behaviour result in amplitude suppression or

enhancement of the EEG signals. The association of

this EEG modulation with specific events is known

as event-related oscillation (ERO). Those events can

be of two types: event-related synchronization

(ERS) and event-related desynchronization (ERD).

If the EEG signals increase their synchrony and thus

their amplitude, an ERS arises. Otherwise, an ERD

appears (Klimesch, 1999). Particularly, MI activity

triggers ERD on the contralateral hemisphere, as

well as ERS on the ipsilateral hemisphere. The

concerned oscillations take place within alpha (8–

12Hz) and beta (16–24Hz) bands over the primary

sensory-motor cortical area (Pfurtscheller and Lopes

da Silva, 1999). As EEG power can reflect ERD and

ERS, there are three methods based on power

measurement to detect MI activity. These are: (1)

band power (BP) or absolute power, (2) relative

power, and (3) ERD-ERS values. All of them were

implemented in the miBCI software and are

illustrated in Figure 2.2c.

BP consists of three steps: (1) band-pass filtering

of the EEG signals in predefined frequency bands,

(2) squaring of the amplitude samples to obtain

power samples, and (3) averaging of the power

samples over specific time segments (Pfurtscheller

and Lopes da Silva, 1999). Note that time segments

used to average the power samples are specified in

the segmentation tool of the data acquisition menu

(Figure 2.2a).

Relative power is defined as the ratio between

the absolute power in a single frequency band, and

the absolute power in a collection of frequency

bands (Kropotov, 2009; Sörnmo and Laguna, 2005).

This is determined as follows. First, the EEG signals

are band-pass filtered in predefined frequency bands.

Second, the EEG signals are band-pass filtered in a

broad band that involves all the foregoing frequency

bands. Third, the amplitude samples are squared to

obtain power samples. Fourth, the power samples in

the predefined frequency bands are divided by the

power samples in the broad band. Finally, the power

samples are averaged as in the BP method.

To obtain ERD-ERS values, the same procedure

described for BP is followed. However, having

determined the BP estimates, these are additionally

divided by an average power value. This value refers

to the BP calculation in a reference interval (RI),

which is typically taken a few seconds before

occurring the control task.

2.2.4 Feature Selection

Feature selection is based on two stages: ranking and

classification. This means that the features within

each vector are first ranked from the most to the

least fruitful feature by using Davis-Bouldin index

(DBI) or recursive feature elimination. Having

ranked the features and in order to select a proper

number of them, a classification stage takes place as

follows. First of all, if there are three classes (class

class

, and class

) under study, then one classifier

) is assigned to discriminate between class

1+2

and

class

, while another one (c

) is used to discriminate

between class

and class

. If there are two classes

(class

and class

), then only the first classifier (c

)

is necessary. Applying any of these two

classification methods, the already ranked feature-

vectors are classified every



features,

accomplishing  classifications in total (C



). This

means that K sub-feature-vectors (x) are formed on

the basis of the factor



. In addition to the factor



the feature-indexes (



) corresponding to the first and

last features taken from every feature-vector must be

defined as well. The whole classification stage can

be expressed by

1),::(1),::(

321











lastfirstclasslastfirstclass

xxc



(1)

From the  resulting classification accuracies, the

miBCI software searches the classifier(s) that yields

the highest classification accuracy(ies), and so the

most fruitful features are selected.

2.2.5 Classification

Classifiers mainly seek to assign a feature-vector to

a specific class through a discriminant function. In

the miBCI software, this function is obtained by

using Fisher discriminant analysis (FDA) or

linear/Gaussian support vector machines (SVM).

The classification stage in the miBCI software

proceeds in five steps. First, the feature-vectors are

scaled to avoid features in greater numeric ranges

dominate those in smaller numeric ranges. The

feature-vectors are normalized by using the mlpy

module (Albanese et al., 2012) or standardized by

dividing MI-related features by RI related features.

PhyCS2015-2ndInternationalConferenceonPhysiologicalComputingSystems

Figure 2.2: Graphical user interface of the miBCI software. The software was structured in four tabs. These are: (a) data

acquisition for offline and online analyses, (b) digital signal processing, (c) feature extraction, feature selection and

classification, and (d) plotting tools.

Second, the model is adapted to a training set by

minimizing the difference between a target vector

and the model output of the feature-vectors. This

step is called training phase and is executed through

10-fold cross-validation. In addition, a regularization

process is run during the training phase so as to

prevent over-fitting problems due to the

manipulation of numerous feature-vectors. Third, the

model with the lowest parameters and the highest

classification accuracy is selected. Finally, once the

model has been trained, its ability to categorize

correctly a testing set is evaluated (Bishop, 2006;

Hsu et al., 2003).

2.2.6 Plotting Tools

The miBCI software has six plotting tools (Figure

2.2d). Five of them are for offline analysis and the

remaining one is for online analysis. The offline

tools are divided into three categories: (1) graphical

representation of the EEG signals in the frequency

domain, (2) feature distribution, and (3) time course

of ERD-ERS (Pfurtscheller and Lopes da Silva,

1999). The online tool creates x-y plots of the

incoming features during the brain-computer

communication.

2.3 Evaluation of the System

Three naive participants (one female and two males)

took part in a pilot study. All of them consented to

take part in the study and none of them reported

neurological deficits. All the participants were aged

between 26 and 31 and were right-handed. The study

lasted around 50 minutes split in a 30-min session to

mount 61 electrodes, and a 20-min session to train

the participants. The 61 electrodes corresponded to

the EEG layout of 81 electrodes based on the 10/10

system, wherefrom the 20 ones localized along the

0% axial reference curve were discarded. The

participant training session was arranged in four

runs. Each run had 40 trials (20 left and 20 right

hand MIs) and each trial lasted between 10 and 11s

(Figure 2.3).

The miBCI software was applied to monitor the

behaviour of the EEG signals during the experiment

(online data analysis), and subsequently, was also

applied to analyse those EEG signals (offline data

analysis). In both analyses, the time windows taken

by the miBCI software were MI performance (from

3 to 7s) and relaxing period (only from 8 to 10s).

The relaxing period was used to characterize the no

control (NC) class in the online data analysis, while

ImplementationofaMotorImagerybasedBCISystemusingPythonProgrammingLanguage

it was used as RI in the offline data analysis.

2.3.1 Online Data Analysis

For the online data analysis, the miBCI software was

configured as follows. Two bipolar channels (FC3–

CP3 and FC4–CP4) were selected. The MI-related

signals were segmented using 1s time windows,

while those related to NC were segmented using

500ms time windows. In both cases, no overlapping

was applied. The feature extraction was based on

absolute power measurements within two narrow

frequency bands: upper alpha (

U, 10–12Hz) and

upper beta (

U, 20–24Hz). Given the configuration

described above, feature-vectors of 16 features were

obtained.

2.3.2 Offline Data Analysis

For the offline data analysis, the miBCI software was

configured as follows. Three central channels (C3,

Cz, and C4) were taken and spatially filtered via two

methods: small Laplacian and CAR. The channels

were segmented by using time windows of 1s length

with 50% overlapping rate. The feature extraction

was based on absolute power measurements within

four narrow frequency bands: lower alpha (

L, 8–

10Hz), 

U, lower beta (L, 16–20Hz), and U. The

classification was executed through a Gaussian

SVM, which was trained with 40 trials per class and

tested with 40 trials per class as well. This

configuration resulted in vectors of 84 features.

2.4 Application of the System

To demonstrate the usability of the miBCI software,

the conduct of two independent studies, where the

software was applied, is hereunder outlined.

2.4.1 Analysis of the Cue Effects

The aim of this analysis was to investigate the cue

(audio, visual and bimodal stimuli) effects on left

and right hand imaginary movements.

Nine participants (four females and five males)

took part in this study and signed a consent form. All

of them were aged between 28 and 41 years. None

of them reported auditory impairments, seven of

them had normal vision, and two of them had

corrected-to-normal vision. Eight of the nine were

right-handed and only one was left-handed. The

participants attended two sessions, which lasted 48

minutes each and followed an identical procedure.

Every session consisted of six runs and one run had

50 trials. One trial took from 8500 to 9500ms. Each

trial consisted of three phases: movement

preparation (0-2500ms), MI (2500-6000ms) and

relaxation (6000-8500±1000ms). The timing

protocol is similar to that depicted in Figure 2.3.

For analysing the MI-related control tasks, the

miBCI software was configured as follows. Sixty

one electrodes were selected (note that the same

EEG layout used in the pilot study was employed).

Figure 2.3: Timing protocol for the pilot study. Each trial

comprised four phases: warning sign, cue onset plus a

beep, blank screen, and random inter-trial interval.

The EEG signals were referenced through the

large Laplacian method and segmented by using

time windows of 500ms length with 50%

overlapping rate. The feature extraction was based

on absolute power measurements within seven

narrow frequency bands: lower theta (

, 4–6Hz),

upper theta (

, 6–8Hz), 

, 

, 

, and gamma

(, 39–41Hz). The resulting feature-vectors were

increasingly sorted by their DBI and classified via

FDA.

2.4.2 Study of the Workload Influence

The goal of this study was to investigate the

workload effects in BCI systems. For this purpose,

users were immersed into a simulated living-

environment with increasingly demanding scenarios.

In this study, three control tasks were used: left and

right hand MIs and non-MI.

Five women and six men took part in this study.

At the beginning of the study, all the participants

were informed about the experimental procedure and

signed a consent form. All of them were right-

handed and aged between 25 and 60 years. None of

them reported auditory impairments and/or

neurological disorders, nine of them had normal

vision, and two of them had corrected-to-normal

vision.

The experiment was divided into three sessions.

Every session lasted between 120 and 180 minutes.

All of the sessions followed three phases: (1) 61

electrode mounting, (2) determination of the

independent alpha frequency (IAF), and (3)

fulfilment of three scenarios per session. The timing

protocol for this experiment was based on trials that

lasted between 7000 and 8000ms. Each trial

PhyCS2015-2ndInternationalConferenceonPhysiologicalComputingSystems

included three phases: warning (0–1500ms), control

task (1500–5000ms), and blank screen (5000–

7000±1000ms). The trial configuration is similar to

that illustrated in Figure 2.3.

For this study, the miBCI software was

configured as the previous study. However, instead

of using predefined frequency bands to the feature

extraction process, theta and alpha bands were

adjusted to the IAF of each participant.

Note that the EEG analysis of the two

aforementioned studies was exclusively offline.

3 RESULTS

3.1 Evaluation of the System

The results that were obtained from the pilot study

conducted to evaluate the miBCI software are

presented in Table 3.1. As can be seen from the

table, the system recognized at least 60% of the MI

patterns of three participants in both online and

offline analyses. The classification results showed

that the small Laplacian and the CAR methods were

more effective to discriminate MI patterns than the

bipolar method. Furthermore, the small Laplacian

method was more efficient than the CAR method.

The results are congruent with those reported in

(Ramoser et al., 2000), thereby demonstrating the

proper functionality of the miBCI software.

3.2 Application of the System

The utility of the miBCI software is illustrated by

presenting some preliminary results obtained from

two offline analyses. As will be described in the next

paragraphs, those preliminary results provided a

valuable insight into the EEG information at hand.

3.2.1 Analysis of the Cue Effects

To illustrate the application of the miBCI software,

feature-vectors proceeding from right hand MI cued

by audio stimuli were selected. As can be seen from

Figure 3.1, right hand MI produced EROs in

unexpected frequency bands such as theta and

gamma. Specifically, right hand MI produced

remarkable ERD on the contralateral hemisphere in



, 

, 

, and  bands. Similarly, it caused

significant ERS on the ipsilateral hemisphere in 



, 

, and

bands. Refer to Figure 3.1.

3.2.2 Study of the Workload Influence

The main objective of this analysis was to observe

the control task changes throughout increasingly

demanding scenarios. In order to exemplify the

miBCI software application, spectral information of

the three control tasks from one of the participants is

presented. The spectrograms of the control tasks

obtained from the lowest and the highest demanding

scenarios are compared in Figure 3.2. It can be seen

that most of the spectral components are held within

0 and 40Hz when the control tasks were generated

under low demanding situations (a, b, and c). In

contrast, spectral components are spread over all the

frequencies when the control tasks were generated

under high demanding situations (d, e, and f).

Table 1: Results of the pilot study.

Figure 3.1: ERD-ERS maps of right hand MI cued by

audio stimuli. The MI activity on right and left hemisphere

is display in (a) and (b), respectively.

Figure 3.2: Spectrograms of left, right, and non-MI control

tasks extracted from the left hemisphere. The control tasks

coming from low demanding situations are display in (a),

(b), and (c) for left, right, and non-MI, respectively.

Likewise, those coming from high demanding situations

are presented in (d), (e), and (f).

ImplementationofaMotorImagerybasedBCISystemusingPythonProgrammingLanguage

4 DISCUSSION

At present, there is a wide variety of BCI software

that is very complete and free open-source.

However, most of the available BCI software runs

under a Matlab environment. This leads to purchase

a MathWorks license. The cost and restrictions of

such license is what hinders the use of BCI software

that requires Matlab programming language. In fact,

a common topic discussed in social networking

websites such as ResearchGate and LinkedIn is the

substitution of Matlab for biosignal processing.

Several scientific fields have been gradually

turning to Python programming language, which

offers all the benefits of Matlab, but under a free

open-source environment. Python is a functional and

object-oriented programming language, which

facilitates software development from scratch. With

regard to BCI research, Python has an extensive

variety of modules applicable to neurosciences,

pattern recognition, machine learning and others.

In the light of the above discussion, a MI-based

system was programmed through Python

programming language. The system was called

miBCI software and was based on online and offline

data analysis. In both analyses, the same EEG data

processing system was adopted. This data processing

system was created in line with six modules: data

acquisition, DSP, feature extraction, feature

selection, feature classification, and plotting tools.

The functionality of the miBCI software was tested

in a pilot study, and its utility was exemplified

through a miscellaneous collection of plots obtained

from two offline studies.

Although the miBCI software is terminated for

now, further work is required to increase the

versatility of the system. A number of future

improvements have been considered. First of all, the

online data analysis of the software can be

redesigned in order to detect non-control stages.

This will allow users to control the miBCI software

at any time. In other words, it is proposed to

transform the synchronous system into an

asynchronous one. Secondly, a larger number of

classes can be included so as to offer greater

freedom of manipulation to the users. Thirdly, it is

worth mentioning that the modules of the miBCI

software are subject to constant improvement.

Examples of such improvement are the following.

The mechanism for loading EEG data in the offline

analysis could be adapted to read BDF-files, and not

only mat-files. The feature selection may involve

other typical methods used in BCI research such as

principal component analysis. The variety of

classifiers can be enriched by including algorithms

such as neural networks.

REFERENCES

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1200907.1682: The Neuromorphic Engineer.

ImplementationofaMotorImagerybasedBCISystemusingPythonProgrammingLanguage