Endogenous Cognitive Tasks for Brain-Computer Interface:

A Mini-Review and a New Proposal

Dize Hilviu

, Stefano Vincenzi

, Giovanni Chiarion

, Claudio Mattutino

Silvestro Roatta

, Andrea Calvo

5,6,7 e

, Francesca M. Bosco

1,7 f

and Cristina Gena

Department of Psychology, University of Turin, Via Verdi 10, 10124, Turin, Italy

Department of Computer Science, University of Turin, Corso Svizzera 185, 10149, Turin, Italy

Department of Electronics and Telecommunications, Polytechnic of Turin, Corso Duca degli Abruzzi 24, 10129, Turin, Italy

Rita Levi Montalcini Department of Neuroscience, University of Turin, Corso Raffaello 30, 10125, Turin, Italy

Rita Levi Montalcini Department of Neuroscience, University of Turin, Via Cherasco 15, 10126, Turin, Italy

Neurology, Hospital Department of Neuroscience and Mental Health, Città della Salute e della Scienza Hospital of Turin,

Corso Bramante 88, 10126, Turin, Italy

Neuroscience Institute of Turin, University of Turin, Regione Gonzole 10, 10043, Orbassano, Italy

silvestro.roatta@unito.it, andrea.calvo@unito.it, francesca.bosco@unito.it, cristina.gena@unito.it

Keywords: Human-Computer Interaction, Brain-Computer Interaction, Brain-Computer Interface,

Electroencephalography, EEG-based BCI, Cognitive Task, Endogenous Task.

Abstract: Brain-Computer Interfaces allow interaction between the voluntarily produced human cerebral activity and a

computer. The output produced by the user’s performance can serve as an input to the technologic device that

can decode this information and transform it to a command. Literature has usually focused on processing and

classification often neglecting the importance of the mental tasks used to elicit and modulate the cerebral

activity. In this paper, we review previous mental tasks used in literature: motor imagery, spatial navigation,

geometric figure rotation, imagery of familiar faces, auditory imagery and math imagery. Then, we propose

a set of these tasks modified to maximize the user’s performance during the execution of mental tasks.

1 INTRODUCTION

Brain-Computer Interaction is a scientific approach

offering various opportunities of empirical research

in the neuroscience domain. This technique uses

special interfaces (Brain Computer Interfaces, BCIs)

allowing the interaction between the human cerebral

activity and an electronic device (Wolpaw et al.,

2002). There are several types of BCIs, each one

based on different methods to detect brain signals

(e.g., Electrocorticography, Functional Magnetic

Resonance Imaging, Positron Emission Tomography,

etc.). Among them the Electroencephalography-

https://orcid.org/0000-0002-4312-2206

https://orcid.org/0000-0001-5588-5633

https://orcid.org/0000-0002-0413-2436

https://orcid.org/0000-0001-7370-2271

https://orcid.org/0000-0002-5122-7243

https://orcid.org/0000-0001-6101-8587

https://orcid.org/0000-0003-0049-6213

based (EEG) method is one of the less invasive. EEG-

based interfaces use a change in brain electrical

activity as an input signal, which is usually defined as

event-related synchronization or desynchronization

(for a comprehensive review on EEG-based BCI

paradigms see Abiri et al., 2019). The change may be

caused by exogenous stimuli (triggered by external

events) or endogenous stimuli (voluntarily produced

by the subject while imagining a movement for

instance) (Tan & Nijholt, 2010). The possibility to

use exogenous stimuli is reduced since they are less

likely to be used by some clinical populations. For

example, persons with complete locked-in syndrome,

174

Hilviu, D., Vincenzi, S., Chiarion, G., Mattutino, C., Roatta, S., Calvo, A., Bosco, F. and Gena, C.

Endogenous Cognitive Tasks for Brain-Computer Interface: A Mini-Review and a New Proposal.

DOI: 10.5220/0010661500003060

In Proceedings of the 5th International Conference on Computer-Human Interaction Research and Applications (CHIRA 2021), pages 174-180

ISBN: 978-989-758-538-8; ISSN: 2184-3244

i.e., a neurological disorder that causes the complete

paralysis of all voluntarily muscles but spares the

cognitive functionality, could not perform a visual

task but instead will use a somatosensory paradigm,

such as vibro-tactile or auditory (De Massari et al.,

2013; Guger et al., 2017; Halder et al., 2016).

Therefore, endogenous stimuli are more suitable to

involve a larger sample of subjects that could benefit

of this technology. Specifically, cognitive tasks are

the most used and they consist in mental tasks where

the user is asked to imagine something or doing

something.

Cognitive tasks must be selected considering

several aspects, since high individual differences in

responsiveness to the task have been reported

(Friedrich et al., 2012).

The first element to consider is the preferences of

the users: based on their personal past experiences,

users may find easier to perform some tasks than

others (Kleih & Kubler, 2016; Lotte et al., 2013).

Second, since BCI technology is mostly used in

medical/rehabilitative contexts, another aspect that

should be considered is the residual cognitive ability

possessed by participants (De Massari et al., 2013;

Kübler & Birbaumer, 2008). Several pathologies can

impact on the cognitive functioning, thus patients in

locked-in condition may find difficult to imagine

body movements (Birbaumer & Cohen, 2007).

Third, another crucial aspect is the set of

psychological variables that could affect the task

performance (Kleih & Kubler, 2016), as for example,

the fatigue and frustration that may come from the

effort of the task realization, or mood and motivation

in performing the task (Kleih et al., 2010).

Finally, the self-regulatory skills, i.e., the ability

to be concentrated, focused and the ability to decide

how much attention direct towards some activities by

ignoring other distracting stimuli must as well be

considered (Kleih & Kubler, 2016). All these

cognitive abilities (i.e., updating, shifting, attention

and inhibition), known as Executive Functions, allow

people to perform goal-directed behaviours and

reside in the prefrontal cortex (Miyake et al., 2000),

which is reported to be damaged in some pathological

cases, e.g. Alzheimer disease.

Chances to perform a fine classification of the

acquired cerebral signal are increased further when a

training precedes the recording sessions (Lotte et al.,

2013). During the training users learn how to control

and regulate their performance (EEG signals). The

training session is particularly important in the

experiments involving patients with a pathological

condition that may aggravate in time, such as patients

with amyotrophic lateral sclerosis that may be in a

locked-in condition and, after the worsening of the

disease, may move to a complete locked-in condition

where muscular abilities are permanently impaired

(Neumann & Kübler, 2003).

Literature has focused mainly on the elaboration

and classification processes often neglecting the

cognitive task design or selection process which can

promote an optimization of the BCI performance by

selecting the most appropriate strategy for each user

(Curran & Stokes, 2003; Friedrich et al., 2012;

Lazarou et al., 2018). The aim of the present study is:

first to provide a short review of the cognitive

endogenous tasks used in previous research (motor

imagery, spatial navigation, geometric figure

rotation, imagery of familiar faces, auditory imagery

and math imagery) and then to propose some

revisions to these tasks, based on the literature

available for empirical investigation in this domain.

2 COGNITIVE TASKS

2.1 Motor Imagery

Motor imagery is the most widespread paradigm used

to elicit a change in the cerebral activity. Subjects

have to imagine repetitive movements of their own

arms, hands, legs or feet. Movements can involve the

imaginary use of objects (e.g., shift an object, squeeze

a ball) or they can simply be a movement of the body.

This paradigm is largely used because its results are

more reliable than those produced by other tasks

(Attallah et al., 2020; Curran et al., 2004; Friedrich et

al., 2013; Lu et al., 2020; Togha et al., 2019). This

high statistical discrimination is easily explained

since the planning of a motor movement, activates

brain areas (primary motor cortex, supplementary

motor area and premotor cortex) clearly identified in

the neuropsychological literature (Moran & O’Shea,

2020). See Table 1.

However, as already mentioned, motor imagery

tasks may not be suitable in some cases, e.g., in

locked-in syndrome, where patients are not able to

perform movements and may find difficult to imagine

how to program and execute a movement.

2.2 Spatial Navigation

Another cognitive task often used in literature is the

spatial navigation (Cabrera & Dremstrup, 2008; Lugo

et al., 2020): subjects have to imagine being in a

familiar place, such as their own house, and to move

from a room to another. Specifically, the task requires

participants to focus on surrounding objects and not

Endogenous Cognitive Tasks for Brain-Computer Interface: A Mini-Review and a New Proposal

175

on walking, otherwise an overlapping with the motor

activity may take place (Curran et al., 2004).

However, some other studies asked participants to

focus on orientation (Friedrich et al., 2013).

Differently from the motor imagery task, which

activates a specific brain area, spatial navigation

imagery activates several brain regions, i.e., the

dorsal fronto-parietal regions, presupplementary

motor area, anterior insula, and frontal operculum

(Cona & Scarpazza, 2019). See Table 1.

2.3 Geometric Figure Rotation

In the geometric figure rotation task, participants are

asked to think about the rotation of an object (such as

a cube) on a specific axe. In certain cases, the task can

be hard to perform, therefore an example of rotation

is provided, i.e., participants are provided for few

seconds with a video where an object is rotating and

then asked to imagine the movement (Anderson &

Sijercic, 1996; Huan & Palaniappan, 2000; Lee &

Tan, 2008; Rahman & Fattah, 2017). A general

consensus exists in neuropsychological literature in

considering the parietal cortex as the core region of

activation for this task (Jäncke & Jordan, 2007). See

Table 1.

2.4 Imagery of Familiar Faces

Another task quite often used in literature to stimulate

cerebral activity involves the imagination of the face

of a dear person or a famous celebrity (Başar et al.,

2007; Friedrich et al., 2012; Özgören et al., 2005).

This task recruits several brain regions (e.g.,

parahippocampal gyrus, middle superior temporal

gyri, middle frontal gyrus) depending on the type of

stimulus (faces of parents, partners etc.). This task

usually activates the fusiform gyrus (Taylor et al.,

2009). See Table 1.

2.5 Auditory Imagery

Thinking to a familiar tune or song has also been

proposed and used as cognitive task. Here, subjects

are usually instructed to “sing” in their head the song

without moving the mouth or any other body parts (to

avoid an overlapping of the motor activity) (Cabrera

& Dremstrup, 2008; Curran et al., 2004; Gonzalez &

Yu, 2016). This task activates the auditory cortex

(Kraemer et al., 2005). However, many elements of

the task might recruit other areas such as the left

hemisphere if the user is imagining songs with words

or the supplementary motor area if the songs includes

humming (Halpern, 2003). See Table1.

2.6 Math Imagery

Math imagery includes two different types of tasks:

math calculations tasks and visual counting tasks.

In math calculations, subjects are given the

instruction to think of some additions, subtractions or

multiplications and are asked to solve the calculations

without producing vocalisations or muscular

movements (Han et al., 2019; Roberts & Penny, 2000).

In visual counting tasks, participants are asked to

imagine numbers written sequentially on a

blackboard. They are specifically instructed to think

of a number, then erase the number, and image the

next one being written on the blackboard. As for the

others tasks, subjects are not allowed to produce

verbalizations or muscular movements (such as lips

counting) (Huan & Palaniappan, 2000; Rahman &

Fattah, 2017).

Math calculations tasks involve both frontal and

parietal areas (Arsalidou et al., 2018). See Table 1.

Table 1: Brain areas activated in function of the cognitive

task.

Cognitive tas

Brain areas

Motor imagery

Primary motor cortex,

supplementary motor

area,

remotor cortex

Spatial navigation

Dorsal fronto-parietal

regions, presupplementary

motor area, anterior

insula, frontal operculu

Geometric figure rotation Parietal cortex

Familiar faces imagery

Parahippocampal gyrus,

middle superior temporal

gyri, middle frontal gyrus,

fusiform gyrus

Auditory imagery Auditory cortex

Math ima

Frontal and

arietal areas

3 A PROPOSAL OF REVISED

TASKS

The short review described in the previous section

briefly summarizes the type of tasks that are currently

used in EEG-based BCI literature.

On this basis, we propose a list of tasks inspired

by those present in the literature but with some

modifications in order to overcome the problems that

could negatively affect the participant’s performance

as the indecision and stress that the user may

experience while choosing what kind of movement to

perform. On the other hand, such tasks proposal

promotes and encourage attention and concentration

CHIRA 2021 - 5th International Conference on Computer-Human Interaction Research and Applications

176

and can be considered as specifically tailored since

users can select the one they prefer.

There are several other aspects of novelty that

characterize the tasks that will be described in the

next section. First of all, the proposed tasks are

preceded by the description of training sessions that

help the user exercising and controlling her/his

output. Although there is no specific indication on the

duration of trainings (Roc et al., 2021), we grounded

our proposal on the existing and previously cited

literature and empirical research. Therefore, we

suggest to perform at least 3 sessions of training, until

users report to feel confident in mentally executing

the task. Secondly, the execution of each task is

followed by a questionnaire where users have to

indicate, using a Likert scale (from 1 to 5), how

confident they felt during the task and how easy they

found to imagine that specific task (1 = not

comfortable/very hard to imagine; 5 = very

comfortable/ very easy to imagine).

3.1 Motor Imagery Task

The user is asked to imagine doing a hand movement

without any muscular movements, specifically to

imagine her/his left hand moving to the left or the

right hand to the right. In the training, users sit in front

of a computer screen and see two hands, right and left,

with palms down from a self-centred perspective.

Subjects have to imagine moving one hand at a time,

depending on how it will be requested and indicated

by an arrow: the right hand will move towards the

outside of the display to the right and the opposite for

the left. After 1 second of image presentation (hand +

arrow indicating direction of movement), the user has

to imagine the movement of her/his hand and then, in

order to strengthen the imagination, the participant

sees the movement on the screen (the duration of the

movement is 6 seconds). Each trial of the training

lasts 10 seconds, and the complete training comprises

8 runs with 18 trials each.

3.2 Spatial Navigation Imagery Task

Differently than the existing tasks based on

navigation imagery, the spatial navigation task

consists of 2 subtasks, based on the participant’s

preference: a navigation with an egocentric

perspective and one with an allocentric perspective

(Tversky, 1991).

The egocentric perspective (also called route)

refers to the point of view of the participant as she/he

is inside an environment and has to move to the left

or the right. Here users have to imagine themselves

while moving within a familiar environment from

their point of view, e.g., their house or the hospital. In

the training session, users sit in front of a computer

screen and look at some videos (video games mode).

Videos show an environment for few seconds (still

image) and, in this fraction of time, users have to

imagine themselves moving forward according to the

path suggested from time to time by the images, e.g.,

the image shows a room with only one door to the

right. After that, the video shows the movement (e.g.,

entering thorough the door to the right, the only one

visible).

The allocentric condition (also called survey)

refers to the perspective from above, such as when

looking at a map/labyrinth. In this task, users have to

imagine a cursor moving in a map where only a path

is visible and therefore possible. In the training

session, users sit in front of a computer screen and

visualize a video showing a map with a cursor moving

step by step. The user is asked to imagine the

movement of the cursor through the path.

The training of both sub-tasks consists of 8 runs

with 10 trials each. Each trial is characterised by 6

seconds of movement imagination and 3 seconds of

movement visualization. Each run lasts 90 seconds

and turns (left and right) are balanced in order to

avoid any kind of bias. After training, users are

requested to use this kind of spatial navigation task

during the recording session.

3.3 Object Rotation Imagery Task

In this task, users have to imagine a familiar object

rotating. Unlike previous tasks, we introduced a real

object to be used in the training session, i.e., an

hourglass, because we believe that movement of the

sand can help the user to imagine the rotation. The

user sits in front of a computer screen and visualizes

the hourglass rotating clockwise or counter

clockwise. The training session is characterised by a

series of images, in each of them an indication of the

future rotating movement is placed. Three seconds

are provided to the user to imagine the movement and

then the rotation is shown and lasts 6 seconds. The

training comprises 8 runs with 18 trials (rotations)

each.

3.4 Face Imagery Task

In this task, subjects have to imagine the face of a

celebrity. They have to imagine with attention the

eyes, the mouth, the nose etc. The user can choose the

celebrity from a list of famous people. For our

proposal, we have selected 6 (3 females) national and

Endogenous Cognitive Tasks for Brain-Computer Interface: A Mini-Review and a New Proposal

177

international persons (such as Roberto Benigni, Lady

Diana, etc.). This task can be preceded by a training

session where the user sits in front of a computer

screen, images are shown for 10 seconds and then the

participant is asked to recall the face of the celebrity

during the recording session. This process is repeated

6 times. We suggest to propose the celebrities in line

with the age of the participant since young celebrities

may not be familiar to older persons. If the user is

unable to choose due to a pathology, the celebrity can

be chosen by a family member.

3.5 Music Tune Imagery Task

In this task, users have to imagine a music tune but

since the elaboration of the output produced by this

task is highly subjective and perhaps complicated, we

propose to present a list of very famous songs based

on the cultural context (e.g., for the Italian context we

propose “Volare” by Domenico Modugno or

“Azzurro” by Adriano Celentano) among which the

user can choose the preferred one. In the training

session, the user can familiarize with the song by

hearing it 3 times with the lyrics. Then, the user has

to imagine the song without verbalisations or

muscles’ movements.

3.6 Math Counting Task

In the present task, participants have to perform

calculations, without verbalizations or muscular

movements. Users can imagine a number and then

starting to subtract or add a specific number as many

times as requested in the recording process. This task

can be preceded by a training where the user sits in

front of a computer screen and visualizes very simple

math operations: by starting from a specific number

presented on the screen, the user has to add or subtract

a unit (such as 2 or 3), e.g., 9+3 = 12 + 3 =15 etc. The

kind of operation (subtraction or addition) is indicated

before the start of the training session. After carrying

out the operation mentally in 4 seconds, the user sees

the result. The training session is made up of 6 runs,

with 20 trials (calculations) each (the starting number

and the number to add or subtract can change).

4 CONCLUSIONS

EEG-based BCI is a recent technology using brain

activity to allow communicative interactions. In

several cases, there is the need to code for numerous

information therefore different tasks that convey

information are implemented. In the present study we

have summarized the main endogenous cognitive

tasks used in literature: motor imagery, spatial

navigation imagery, geometric figure rotation

imagery, familiar face imagery, auditory imagery and

math calculations imagery. We have then proposed

some adjustments to them, in order to improve the

user’s performance, i.e., the generation of the EEG

signal. First of all, we have added a training before all

the tasks. Second, we propose to ask users using a

questionnaire, the perceived level of confidence and

ease they felt while imagining each task. The

information gathered with the questionnaire will be

useful to direct the future use and application of

mental tasks. Third, the tasks are enriched with details

that users can find more suitable for them and

therefore improve their performance.

Cognitive tasks represent a great resource in this

research area. BCI technology combined with the

availability of several tasks can also contribute to the

cognitive assessment of subjects who are not

completely responsive without involving

verbalizations or muscular movements (Cipresso et

al., 2012; Lugo et al., 2020).

In conclusion, the possibility to choose among

different types of cognitive tasks (and also a preferred

mode such as route vs survey or the face of the

celebrity or the song) provides many benefits to the

cognitive performance of the users. Users can indeed

choose the one they consider most suitable for them.

Furthermore, the high number of tasks can also be

used to code different answers.

Future empirical research should evaluate the

validity of these modified tasks, and should compare

these tasks on the same group of subjects in order to

verify which one maximizes the participant’s

performance also considering the goodness of the

underlying algorithm.

ACKNOWLEDGEMENTS

This work has been funded by the project BciAi4Sla,

Brain computer interfaces and Artificial intelligence

for amyotrophic lateral Sclerosis, funded by

Fondazione CRT, Torino, Italy.

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