Designing a Brain-Computer Interface to Chess
Hermen Toersche
h· a· toersche@student· utwente· nl
ABSTRACT
Brain-computer interfaces (BCI) offer an additional communication channel, which may be applied to control a chess
game. This paper describes the design of a brain-computer
interface to chess based on a 8 × 8 P300 speller. The system
encodes a move in the chess game as selecting a start and a
destination position on the chess board. Most of the design
can be implemented using existing hardware and software.
Move selection is expected to take about 30 seconds per
move at over 95% accuracy.
with almost no time constraints can be used. Most turnbased games require little user input and coarse limits are
imposed on the time required to perform the input.
A possible suitable candidate is chess. Chess is a ‘slow’
game, requiring user input only after extensive elaboration
of the move to make. Multiple open source implementations are available, allowing for extension without having to
program a complete game.
2.
Keywords
Brain-computer interface (BCI), chess, electroencephalography (EEG), P300, BCI2000.
1.
INTRODUCTION
The locked-in syndrome (LIS) is a collective name for brain
conditions which cause patients to lose control of the abilities
to move and speak, while leaving the mind intact. To help
LIS patients, brain-computer interfaces (BCI) have been developed. A brain-computer interface allows a person to communicate with the computer without performing a physical
action, such as dragging a mouse or pressing a button. Instead, brain activity is measured and interpreted. Most
current BCI systems are based on noninvasive electroencephalography (EEG) measurements, reaching information
transfer rates in the order of tens of bits per minute. Currently, BCI is still used as a ‘last resort’ input channel, which
can be applied when all else fails.
PROBLEM STATEMENT
Almost all current BCI designs are targeted at disabled individuals [MBF+ 07]. This focus has caused that most BCI
applications are aimed at providing a prothesis for rehabilitation purposes. When cost and effort to use a BCI decline,
a brain-computer interface may also become of interest for
healthy individuals. Games may be used to explore how
BCI can be used by healthy people.
The chess game is an application that is also of interest for
healthy individuals. The main problem addressed in this
paper is the design of a BCI-controlled chess application:
How can a brain-computer interface be designed to control
a chess game?
In order to determine the usefulness of the designed system,
the following questions should also be answered:
• How much time does it take to make a move?
While initially developed for the disabled, BCIs may also be
used by healthy individuals to replace normal input devices.
For instance, BCI is gaining attention from the gaming research community. Brainball has demonstrated that BCI
can be applied for games [HB00]. Experiments have been
performed with control tasks such as balancing a virtual
character on a tightrope in an immersive 3-D environment
[LKF+ 05]. Aside from control tasks, the EEG signal may
be used to monitor the affective state of an user. BCI control in gaming applications is generally aimed at augmented
control by healthy persons.
To deal with the low information transmission speed provided by current BCI technologies, a turn-based game model
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• How reliable can moves be communicated?
3.
BRAIN-COMPUTER INTERFACES
Brain-computer interfaces are a relatively new kind of computer input devices. Therefore, knowledge about the principles and functioning are not yet widely dispersed. This
section provides a glance at important aspects of BCI technologies.
3.1
Brain wave acquisition
Brain-computer interfaces need an interface from the brain
to the computer. To enable ‘mind control’, the computer
must know what is going on in the mind of the subject by
using some biorecording technology. Several biorecording
technologies exist, each with their own characteristics with
relation to spatial resolution, temporal resolution, cost, and
risk [HD06].
Electroencephalography (EEG) is a non-invasive biorecording
technology, which measures electrical activity of the brain
using electrodes [Wika]. EEG has low spatial resolution,
high temporal resolution, relatively low cost and minimal
risk. Due to these properties, EEG is the most used medium
for BCI [MBF+ 07]. A major disadvantage of EEG is its susceptibility to background noise, reducing information transfer rates to 5–25 bits per minute [WBH+ 00].
3.2
P300 speller
One of the earlier brain-computer interfaces is the EEGbased P300 text speller by Farwell and Donchin [FD88]. The
P300 speller is based on the P300 component of the eventrelated brain potential. The human brain emits a P300 peak
when an event occurs that is task relevant or novel [Wikb].
This peak can be detected using EEG.
The P300 speller presents a 6 × 6 matrix containing the
letters of the alphabet and control commands. The user is
instructed to focus on a letter and to keep a running mental
count of the number of times it flashed. The system will
consecutively flash the rows and columns of the matrix (see
Figure 1). When the row or column containing the focused
letter is flashed, the subject’s brain will emit a P300 peak,
because counting the number of flashes makes the occurred
flash task relevant.
a time. According to Farwell and Donchin, “it is also obvious that the process would be unacceptably slow” [FD88],
because it needs 6 × 6 = 36 instead of 6 × 2 = 12 flashes
to cover all matrix items. However, the amplitude of the
P300 decreases when stimulus frequency increases, so the
row/column speller needs to average more trials than the
cell speller to reliably determine the selection.
3.4
BCI 2000
BCI 2000 is a general purpose brain-computer interface research and development platform. The framework offers
modules to perform common BCI tasks, such as signal acquisition, signal processing and data analysis. Several sample
user applications are provided, such as a P300 speller and a
cursor movement task [SMH+ 04].
The modules of the framework are loosely coupled, allowing
components to be interchanged without affecting other parts
of the system. This approach allows new components to be
added and tested in a systematic manner. Also, components
can be reused in other projects.
BCI 2000 is in use by about 50 groups worldwide [SMK+ 05].
Ideally, the system could determine which letter was focused
after all rows and all columns have been flashed once by
choosing the row and column with the strongest P300 response. Due to the low signal-to-noise ratio of EEG, multiple trials of row and column flashes need to be averaged to
reliably detect the P300 peak.
The original P300 speller allows subjects to accurately spell
at a rate of 2.3 characters per minute [FD88]. In a reimplementation of the P300 speller using modern components,
healthy subjects could spell at a rate of 4.3 characters per
minute at 95% accuracy [DSW00].
One of the main advantages of the P300 speller is that minimal training is required to use the system. In contrast,
the Thought Translation Device takes months of training
to achieve 75% accuracy for binary choices [BKG+ 00]. The
disadvantage of the P300 speller is the questioned usability
for locked-in patients [DSW00]. Vision problems are common with LIS patients, as horizontal gaze control is often
lost [PG86].
3.3
P300 cell speller
According to [GTW04], a cell speller will increase spelling
performance significantly compared to the Farwell-Donchin
row/column speller. The P300 cell speller is the same as the
classic P300 speller, except that it highlights one letter at
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3.5
Chess BCI
Building a brain-computer interface to chess has already
been attempted by Samuel Inverso [Inv04]. His selection of
the chess domain was, among other reasons, motivated by
a collaboration with researchers at the Institute of Medical
Psychology and Behavioral Neurobiology (IMPBN) at the
University of Tübingen: one of their patients had requested
an interface to play chess using the Thought Translation
Device [BKG+ 00]. The attempt to build the chess BCI has
halted due to hardware failure.
4. REQUIREMENTS
4.1 BCI requirements
A chess application does not justify implanting BCI hardware. Therefore, the system must use noninvasive BCI, except when an invasive BCI system is already available.
4.2
Application requirements
Inverso has identified the following requirements for a chess
BCI during his collaboration with the researchers at the
Institute of Medical Psychology and Behavioral Neurobiology (IMPBN) at the University of Tübingen (section 3.5;
[Inv04]):
1. Play chess against the user at various levels of expertise.
2. Allow the user to play against a human using the same
computer.
3. Allow the user to play against a human across a network, preferably interfacing with an online chess program such as www.chessclub.com.
4. The program must interface with the user’s current
brain-computer interface (BCI2000).
Figure 1: Row flashes (left) and column flashes
(right) in the P300 speller. Based on [FD88].
5. The chess application must run under Windows and
interface with Borland C++ Builder 5.5 code.
Figure 2: Chess column flash. Edited screen capture
from [Man03a].
Figure 3: Chess cell flash. Edited screen capture
from [Man03a].
For research purposes, most of these requirements may be
dropped. However, a system satisfying these requirements
is more useful than one that does not.
flashed and at most 27 destination cells have to be flashed.
Also, the P300 emitted by counting the number of flashes of
a cell is larger for 16 cell flashes than for 8 row and 8 column flashes, because the stimulus frequency is twice as low,
reducing the number of trials needed for the cell speller.
4.3
System design
It must be possible to distribute parts of the system to separate computers in order to distribute resource load. The
modules of the system should communicate using ubiquitous
protocols. The modules should be loosely coupled to allow
for replacement of individual components without affecting
other parts of the system.
5. CONCEPTUAL DESIGN
5.1 BCI input paradigm
In this paper, I will focus on the P300 speller paradigm
(section 3.2; section 3.3). The P300 row/column speller has
been researched relatively well, and software and procedures
exist to leverage the P300 response. A design based on the
P300 cell speller is also described, as it is almost the same
as the row/column speller.
5.2
P300 row/column speller
In the traditional Farwell and Donchin P300 speller [FD88],
the user can select a character from a 6 × 6 matrix (see section 3.2). The 6 × 6 matrix can be replaced with a 8 × 8
matrix [AP03]. A chess move can be composed by successively selecting a source and a destination cell on the chess
board. Figure 2 shows flashing column d.
Generally, the number of cells that need to be flashed is lower
than the 16 row/column flashes. Increasing the likelihood of
a relevant flash (which will happen when fewer stimuli are
displayed) reduces the strength of the P300 emitted in a trial
[FD88]. It may be necessary to intentionally insert ‘useless’
flashes to increase P300 response for valid cells, especially in
the destination selection phase. Often, only a few destination positions are valid. The number of trials required for a
sufficiently reliable P300 response must be adjusted to the
number of flashes per trial.
A source cell flash is shown in Figure 3.
5.4
High-level design
The high-level design of the system has the following components:
• Chess game
• Chess board view
• P300 BCI
• BCI ↔ chess logic
Using this method, most of the 8 × 8 = 64 offered options
are invalid moves. At most 16 pieces may be moved by the
player, and a piece may move to at most 7 × 3 + 6 = 27
positions (queen at d4, e4, d5 or e5). This redundancy can
be taken advantage of to increase reliability, as invalid moves
may be discarded.
The high-level design of the system is depicted in Figure 4.
game state
chess game
5.3
P300 cell speller
Use of the P300 cell speller (see section 3.3) may significantly reduce the amount of time needed to make a move.
A straightforward cell speller would successively offer 64
source positions and 64 destination positions. By only flashing valid cells, at most 16 source cell positions have to be
request flashes
BCI ↔ chess logic
move
P300 BCI
EEG
selected field
chess board view
flashes
Figure 4: High-level system design
5.4.1
Chess game
The chess game component should manage the general game
flow. It assigns turns to the players, and it performs the
moves offered by the players on the chess board.
5.4.2
Chess board view
The chess board view displays the state of the game. Also,
it displays the flashes commanded by the P300 BCI.
5.4.3
P300 BCI
The P300 BCI component performs the P300 speller selection procedure. It picks a flash sequence, commands the
chess board view to display the flashes and analyzes EEG
input to determine which cell has been focused.
5.4.4
BCI to chess logic
The BCI ↔ chess logic component connects the P300 BCI
to the chess game. Requests for a move from the BCI player
by the game are translated to cell selection requests for the
P300 BCI. The selected cells will be offered as moves to the
chess game.
5.5
Control flow
When the player gets their turn, the player is given some
time to determine which move to make. After that, the
system requests the P300 module to flash the source cells
on the chess board (see section 3.2; section 3.3). The P300
module will report which cell has been focused by the player.
If the selected cell does not contain a piece which may be
moved, the input procedure is repeated. If the selected cell
contains a piece which may be moved, the destination cell
can be selected analogously. The system will compose the
selected source and destination cell into a move, which will
be performed in the game.
5.6
Timing
In order to determine which move to make, a chess player
needs time to think. The amount of time taken may vary
greatly, depending on factors such as player experience, the
presence of time control and the situation on the board.
When using a BCI system that is not self-paced (for more
information on temporal control paradigms, see [MBF+ 07]),
such as the P300 speller, it is unclear at what time the system should offer the opportunity to communicate a move.
There are several approaches to work around this limitation.
The not self-paced BCI may be extended with a self-paced
BCI. The player can request to perform a move using the
additional BCI. For example, the user can imagine repetitive
hand movement [BDK+ 06] to indicate the intention to make
a move. This approach would require multiple BCI applications to be activated in succession, preferably using the
same BCI hardware. It is unclear whether this is currently
possible.
Instead, the available time per move may be limited to a
fixed quota. Using this approach, the offered time can be
either too much or too little. When too much time is offered,
the user has been able to choose the desired move, but time
is wasted while waiting for the start of the move selection
procedure. When insufficient time is offered, the move selection procedure is initiated before the user can choose a
move. When suddenly confronted with the selection procedure, the player may be unable to choose any adequate
move at all.
The problem of offering insufficient time may be alleviated
by reporting the amount of available time to the player.
The player may adapt the decision process to the available
time. The time reporting mechanism should be inobtrusive
in order not to break the player’s concentration on the chess
game. The time left for the current move could be reported
in a way similar to how the remaining game time is reported.
After this time has passed, the player may opt to extend the
move choice time by selecting an explicit option to do this
in the move selection procedure. This option can be offered
as a separate field outside the chess board view. However,
such an out-of-board button increases complexity of the selection procedure for the P300 row/column speller. The
row/column speller would need to handle it separate from
the rest of the board fields, as the button does not have a
row/column position.
The straightforward solution to this problem is to extend the
chess board to a 9 × 9 grid. One or more of the new fields
may be used to offer the ‘more time’ option. While effective,
this solution will increase the time needed to select a move.
Most of the fields on the chess board are empty (at least 64−
2 × 16 = 32 fields), and therefore not valid as fields to start
a move. One of these fields may be replaced with a ‘need
more time’ button. For the row/column speller, this solution
increases selection speed compared to both the ‘extra field’
and the 9 × 9 grid solution, because no extra fields have to
be flashed, while being only slightly more complex than the
9 × 9 grid solution.
For the cell speller, an out-of-board button is not a significant problem. The ‘need more time’ button could be offered at the place where the remaining move choice time is
reported.
The offered time should be adapted to the player and the
board state. A slow player would ‘need more time’ for every
move and a fast player would have to wait needlessly. Also,
it will generally offer too much time for obvious moves and
not enough time for difficult moves.
Note that determining the time needed to choose a move
is not an issue in self-paced brain-computer interfaces. The
user can simply start performing the move.
6.
SYSTEM DESIGN
This section provides a lower level design of the the braincomputer interface system design described in section 5. The
system is designed to use off-the-shelf components wherever
possible. Figure 5 shows a diagram of the system design.
6.1
Module communication
Direct connections between modules should be avoided to
limit module dependencies. A bus-based approach can solve
this problem. All modules connect to a bus where messages can be posted, which will be received by all connected
Care should be taken to limit the size of the messages, as
IRC message length may not exceed 512 bytes [OR93]. This
is not expected to become a problem.
proxy engine
moves, game flow
WinBoard
6.2
flashes
message bus
flash proxy
text ticker
EEG device driver
server
chess game model
BCI ↔ chess
logic
BCI2000
P3AV task
external API
Chess game
The chess game logic can be offered by an existing chess application. The chess game application must be either open
source or extensible to allow necessary modifications to be
made. The game should preferably be written in Java, as
this is the main programming language taught to CS/HMI
students at the University of Twente. However, no suitable
Java chess game implementations satisfying all of the application requirements (see section 4) seem to exist.
BCI2000 proxy
Figure 5: Chess BCI system design
modules. The bus will not be used for high volume traffic,
although latency should be minimal.
6.1.1
Medium choice
UDP is not suitable as a bus medium, because it does not
guarantee reliable message delivery when messages are sent
between computers. Losing a message may deadlock the
system, as control commands may get lost. TCP would be
a suitable candidate. Clients for this protocol are widely
available, both as programming libraries (POSIX sockets)
and as standalone applications (PuTTY).
Instead of TCP, I propose IRC as the module communication medium. IRC is built on top of TCP, and provides
communication at a higher abstraction level. Clients can
post messages in channels. All clients who have joined the
channel (except the sender) will receive the message. IRC is
successfully used to control applications for malicious purposes in botnets. The IRC protocol is described in [OR93].
Clients for IRC are widely available, both as programming
libraries and as standalone applications. Server software is
available, too.
The server should be run on a nearby computer to reduce latency. As the communication channel will be used to transfer user interface commands linked to BCI input, latency
should not exceed 10 ms.
6.1.2
Protocol
The connected modules can send and receive messages in
the following plain-text format:
GNU WinBoard [Man03a] is a mature, free and open source
Windows chess client written in C. It allows players to play
against a human player, the computer or a network player
on an internet chess server. This chess program satisfies
most requirements, except that it does not yet offer a BCI
interface.
The XBoard protocol is the de facto standard chess engine
protocol [Man03b]. The protocol connects chess AI engines
(such as GNU Chess and Crafty) to chess front-end applications (such as WinBoard and Arena). The system can offer an XBoard-compatible chess engine, to which the chess
application can connect to receive move commands and to
exchange game flow information. This is the same approach
as followed by [Inv04] to communicate moves to the chess
application.
For the P300 interface (described in section 5.2), a graphical view on the board must be provided to display field
highlights. WinBoard already provides a chess board view,
which can be modified. The modified version should accept
flash commands from the BCI.
The flash commands could be communicated using a modified version of the XBoard protocol through the proxy chess
engine. A disadvantage of this approach is that it ties the
capability to flash cells on the chess board to a chess engine
being connected. This may not always be the case, for example in WinBoard’s View or edit game files mode, which
mode may be useful for training purposes needing predefined
board positions.
6.3
BCI connection
The system will use BCI 2000 for BCI input (see section
3.4). Using BCI 2000 allows reuse of others’ components and
vice versa. Most of the design, however, does not depend on
BCI 2000. It is only used for acquiring input and to perform
signal processing.
command (hspacei parameter)*
This format is a compromise between human and computer
readability. Using a human readable format facilitates debugging. The commands can relatively easy be interpreted
using statements such as strtok, strcmp and atoi (or the
equivalents in programming languages other than C). Also,
the format is platform independent. Performance problems
related to interpreting the text messages are not expected,
as the communication channel is not used for high-volume
traffic.
6.3.1
P300 input
The BCI 2000 project provides a P300 I/O and processing
module [HMMS05]. The module could be used to present
the chess board for move selection. In order to use the P300
stimulus output functions, the stimulus board images need
to be generated on run-time, as generating images for all
possible board configurations is intractable. The system
may also present a matrix that does not show the chess
pieces on the board. When not showing the chess pieces,
the user may accidentally choose the wrong positions as the
player cannot verify the positions on the board.
These problems can be worked around by displaying the
flashes with an external view. The view is described in section 6.2. The internal stimulus display view from BCI 2000
is hidden from the user. The StimulusCode will be transported by the BCI 2000 proxy (section 6.3.2) to the chess
view.
6.3.2
BCI 2000 proxy
BCI 2000 offers an external application interface to communicate with existing systems that are not integrated with
BCI 2000 [MS06]. The external interface can be connected
to the chess framework with a module that converts UDP
packets to IRC messages and vice versa. To prevent namespace clashes, messages from and to the BCI 2000 proxy
should be prefixed with bci2000.
The BCI 2000 external interface is also used by Brain Dasher
[Wil06].
6.4
BCI chess logic
The BCI chess logic module (BCLM) links the components
together to a chess BCI application.
6.4.1
Chess game
When the proxy XBoard engine gets his turn (i.e., the other
player has moved), it should report that it is the BCI player’s
turn. The BCLM then knows that the player move procedure should start. To communicate this to the user, the
text “Please choose a move...” will be presented on a virtual text ticker. The text ticker may also be used to present
the amount of time left to choose a move.
When time to choose a move is over (or the user has indicated to start a move), the text “Focus on the piece to
move, and count the number of flashes” should appear for
3 seconds. After that, BCI 2000 must be instructed to
present flashes. BCI 2000 will eventually respond with a
SelectedStimulus. The SelectedStimulus must be converted to a board position. If the board position is not
valid as a start position, the user should be notified (“No
piece at selected position / Piece at selected position cannot
be moved. Please try again.”), and the selection procedure
should restart after a few seconds. If the player made a
mistake (e.g. thinks that a certain move is allowed while
it’s not), the player can get additional time by selecting the
‘need more time’ option in the next try.
When a valid piece has been selected, this piece must be
marked, and a destination position can be selected (“Focus
on the destination for this piece, and count the number of
flashes”). The interface should also offer a ‘cancel start position’ option to undo the start piece selection when the user
sees that wrong piece is marked as the start piece. The ability to cancel the start position will reduce the move error
rate.
6.4.2
Chess logic
BCLM needs to have some knowledge about the rules of
chess. The module has to know which moves are allowed
in order to restrict the positions flashed and to validate
whether the selected positions comprise a valid move. An
obvious way to obtain such information is to consult a chess
engine.
7.
EXPERIMENT SETUP
In order to judge how useful the designed system is, the performance of the system needs to be determined. As the system has not yet been fully implemented, experiments with
the chess BCI are not possible at the time of writing. The
setup of experiments to determine the time to make a move
and move accuracy will be described here.
Estimates of the results for the experiments are reported in
section 8.
7.1
Time per move
In a visual P300 BCI, the time needed to select a field on
the board can be calculated. The following formula may be
used to determine the time needed to select a field:
tselect = overhead + #trials × ITI
The number of trials can be varied freely. The inter-trial
interval (ITI) is limited by the inter-stimulus interval, the
number of stimuli per trial and the analysis epoch (see section 3.2.
Normally, a move can be selected in 2 × tselect . However,
undo mechanisms may increase time required per move, as
users may correct input errors. Correcting a start position
error using the ‘cancel start position’ button in the destination selecting interface (see section 6.4.1), or accidentally
selecting this button, takes an additional 2×tselect . Selecting
an invalid positions costs 1 × tselect .
The input time per move may be measured as follows. A
subject plays chess games using several parameter configurations. For each move, the intent to make a move can
be signalled by overt means to the operator. A locked-in
patient may use a vertical eye movement pattern or blinking for this [BGR79]. The move selection time ends when
destination field selection has finished.
7.2
Move accuracy
Field selection accuracy cannot be determined in the same
manner as field selection time, because it relies on the subject’s P300 response strength. Due to the undo mechanism,
move selection errors are essentially reduced to the errors
made in the destination selection procedure.
There are two types of errors for this application: input ermove errors
errors
) and move selection errors ( selected
).
rors ( selected
fields
moves
Input errors may lead to move selection errors.
The error rates can be measured during the same experiment
which measures the time needed per move. The subject
has to report errors to the operator to allow errors to be
measured. The subject should do this immediately after
the move selection (in the opponent’s time) in order not to
disturb the move input process. For a locked-in subject, a
protocol needs to be established to report errors, similar to
how the time of intent to move is determined in the part of
the experiment that measures move time.
8. EVALUATION
8.1 Time per move
The P300 row/column speller [DSW00] can communicate
4.3 items per minute from a grid with 36 items at 95% accuracy. Using the conclusion of [AP03] that matrix size does
not affect performance, the communication speed for the
chess board is:
log2 36
4.3 items/min36 =
× 4.3 = 3.71 items/min64
log2 64
Communicating a move would take a little more than 2 ×
60
= 32.35 seconds, not taking erroneous input or error
3.71
correction into account. Errors should not significantly affect average move time, as the error rate is 5%.
The cell speller claims to need 13 seconds to achieve 95%
accuracy on the 36-cell board [GTW04]. At most 16 pieces
may be moved by the player, and a piece may move to at
most 27 positions (see section 5.3). Therefore, the start and
destination position may be ‘picked’ out of the 36 available
positions to be flashed. Not taking thinking time into account, selecting a move would take less than 2 × 13 = 26
seconds. Because the number of selectable positions is generally far less than 36, performance should be enhanced even
further. How much performance is improved is unknown and
move-dependent.
8.2
Move accuracy
Using the aforementioned error correction mechanisms and
an accuracy level of 95% for position selection, the chance
of selecting the wrong start position is minimal. The chance
of selecting the wrong destination position is less than 5%,
as the erroneous position selected may be an illegal position
if those are offered.
9. DISCUSSION
9.1 Time control
Due to the slow communication speed of current noninvasive BCI technology, time control in chess games should be
disabled when playing with BCI-enabled players. At many
chess tournaments, time control is obligatory [dE]. Time
control is also often used in online chess games. It would
be unfair to reduce the available time because the input device is slower.
It may be possible to design a technology similar to Timeseal. Timeseal is an application that records the player’s
thinking time, so that network transmission time is not penalized [Kno95]. The move selection time should not be
subtracted from the clock.
9.2
Adaptive behaviour
So far, the BCI interface is minimally adapted to the current
state of the board. The only information used is the current
piece positions and where those pieces are allowed to move.
The interface may be improved by incorporating knowledge
about the move decision process.
9.2.1
Leaking information
When incorporating game knowledge (obtained using artificial intelligence), care must be taken not to leak this information to the user. For example, the player may find out
that a move where a lot of thinking time is offered is more
important than a move where little time is offered. Exposing such information may reduce game entertainment and
violates internet chess server usage policies. To register at
FICS (Free Internet Chess Server), one of the largest internet chess servers, you have to agree that “I will at no time
receive assistance during my games” [Ser].
9.2.2
Move choice time
As stated in section 5.6, the move thinking time should be
adapted to the game state, the chess playing skills and the
style of the player. However, care must be taken not to
leak information about the current move to the player. The
system may use information about the history of the game
to adapt its behaviour, as this information is already known
by the player.
The ‘need more time’ mechanism provides feedback on the
chosen time quota. If the player needs more time, the chosen
amount of time was not enough. The system should increase
the amount of time offered in order to reduce the number
of ‘need more time’ requests, improving efficiency. If the
player does not need more time, the chosen amount of time
was sufficient or too much to make a choice. The system
can increase efficiency by gradually reducing the amount of
time offered. Gradually reducing the amount of time offered
may train the chess player to increase decision speed.
9.2.3
Move selection
For move selection, the probability of moves over the game
board could be used to enhance the interface. However,
the issues related to leaking information apply even more
directly. The system may not suggest moves, or flash expected rows/columns or cells more or less often than others.
Such behaviour would allow an observant player to discover
which moves the AI suggests.
The AI move suggestions can, however, be used to reduce
input errors. As a rather extreme example, it is much more
likely that the player wants to make a move that checkmates
the opponent than a move which gives away the queen. The
information could be used to nudge the P300 signal processing to increase the chance of actually selecting the right
move when the player intends to make the right move.
This approach only works when the moves found by the AI
and the moves performed by the player match. A novice will
almost always play suboptimal moves, so when the signal
processing procedure is instructed to prioritize good moves,
accuracy is actually reduced. The same applies to players
who make better moves than the AI [Rii06].
To repair this problem, a comparison should be made between the moves made by the player in the past and the
moves that the AI suggested. The significance of the predicted moves should accordingly be adapted. The moves
made by the player could also be used as a reference point
to adjust chess engine parameters such as search depth.
10.
CONCLUSION
A brain-computer interface to chess has been designed. The
moves for the chess game can be encoded by selecting a
source position and a destination position. The source and
destination positions can be selected using a 8 × 8 P300
speller.
Considering the input device limitations, the system is expected to perform reasonably well. Not taking thinking time
into account, a player is expected to be able to select a move
at high accuracy in about 30 seconds, and probably even
less. Needing 30 seconds to make a move is a lot compared
to doing it by hand, but the BCI may be the only input
device available to the user.
As the system is designed based on existing hardware and
software, implementation of the system should be possible
in the short term.
In the future, the chess system may be adapted to support other BCI input mechanisms. The player could, for
example, move a piece using 2-D continuous cursor control [FMWP04] or by making a sequence of binary choices
[BKG+ 00, BDK+ 06]. The chess domain may also be used
to experiment with new BCI input methods. It would be
interesting to test whether thinking about ‘pushing’ chess
pieces to the desired position provides adequate EEG data
to select a move.
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[FMWP04] G.E. Fabiani, D.J. McFarland, J.R. Wolpaw,
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[GTW04]
Cuntai Guan, Manoj Thulasidas, and Jiankang
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[HB00]
Sara Ilstedt Hjelm and Carolina Browall.
Brainball – using brain activity for cool
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[HD06]
Leigh R. Hochberg and John P. Donoghue.
Sensors for brain-computer interfaces: options
for turning thought into action. IEEE
Engineering in Medicine and Biology
magazine, 25(5):32–38, September 2006.
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