P300与SSVEP融合的高精度新型BCI方法
180 浏览量
更新于2024-08-26
收藏 2.22MB PDF 举报
本文献主要探讨了一种新颖的混合脑-机接口（BCI）范例，该范例结合了P300和视觉稳态诱发电位（SSVEP）技术。发表于《神经科学方法》杂志（Journal of Neuroscience Methods）2015年第244期，16-25页，该研究由Minjue Wang等人进行，他们分别来自中国科学技术大学的高级控制与优化化学过程实验室、英国雷丁大学系统工程学院的脑体化实验室以及美国加州大学圣迭戈分校的认知科学系的认知神经科学实验室。
P300和SSVEP是两种常用的BCI信号类型，P300（P300事件相关电位）通常在用户对特定刺激做出认知反应时产生，而SSVEP则是通过监测视觉刺激变化时的脑电信号来识别用户的注意力。传统的P300 BCI依赖于用户对特定目标的专注，分类准确度较高，而SSVEP BCI则依赖于视觉刺激的频率，常用于处理多任务或空间导航任务。
这项研究创新地将这两种技术结合，旨在提高BCI的性能和效率。实验结果显示，这种新型混合BCI原型在保持P300高分类准确性的基础上，还能达到与传统SSVEP相当的性能，这意味着它能够在同时处理认知和视觉注意任务时表现出优异的表现。更重要的是，研究发现P300信号并未显著干扰到SSVEP的响应，这表明两者可以有效地协同工作，减少了潜在的干扰因素。
这种混合模式的优势在于它可能扩展了BCI的可用性，使用户能够利用P300的认知优势处理复杂任务，同时利用SSVEP的实时性和空间定位能力，从而实现更高效的信息交流和控制。这对于开发具有广泛应用潜力的辅助技术和康复设备尤其重要，例如针对帕金森病患者的手势控制或者游戏界面的无缝切换。
这篇论文提供了对混合P300/SSVEP BCI设计的重要贡献，它不仅提升了BCI的性能，还展示了如何通过整合不同类型的脑信号来增强用户体验和交互的灵活性。这为未来的研究者们探索更多元化的BCI技术组合，提升人机交互的效能和易用性开辟了新的道路。
Journal
of
Neuroscience
Methods
244
(2015)
16–25
Contents
lists
available
at
ScienceDirect
Journal
of
Neuroscience
Methods
jo
ur
nal
home
p
age:
www.elsevier.com/locate/jneumeth
Clinical
Neuroscience
A
new
hybrid
BCI
paradigm
based
on
P300
and
SSVEP
Minjue
Wang
a
,
Ian
Daly
b
,
Brendan
Z.
Allison
c
,
Jing
Jin
a,∗
,
Yu
Zhang
a
,
Lanlan
Chen
a
,
Xingyu
Wang
a,∗∗
a
Key
Laboratory
of
Advanced
Control
and
Optimization
for
Chemical
Processes,
Ministry
of
Education,
East
China
University
of
Science
and
Technology,
Shanghai,
PR
China
b
Brain
Embodiment
Lab,
School
of
Systems
Engineering,
University
of
Reading,
Reading,
UK
c
Cognitive
Neuroscience
Laboratory,
Department
of
Cognitive
Science,
University
of
California
at
San
Diego,
La
Jolla,
CA,
USA
h
i
g
h
l
i
g
h
t
s
•
The
new
hybrid
P300/SSVEP
BCIs
paradigm
could
obtain
as
high
classification
accuracy
as
the
traditional
SSVEP
paradigm.
•
This
pattern
also
could
obtain
as
high
classification
accuracy
of
P300
as
the
traditional
P300
paradigm.
•
P300
did
not
interfere
with
the
SSVEP
response
using
the
new
hybrid
paradigm.
a
r
t
i
c
l
e
i
n
f
o
Article
history:
Received
20
December
2013
Received
in
revised
form
24
April
2014
Accepted
3
June
2014
Available
online
2
July
2014
Keywords:
Brain–computer
interface
(BCI)
Hybrid
paradigm
P300
SSVEP
a
b
s
t
r
a
c
t
Background:
P300
and
steady-state
visual
evoked
potential
(SSVEP)
approaches
have
been
widely
used
for
brain–computer
interface
(BCI)
systems.
However,
neither
of
these
approaches
can
work
for
all
subjects.
Some
groups
have
reported
that
a
hybrid
BCI
that
combines
two
or
more
approaches
might
provide
BCI
functionality
to
more
users.
Hybrid
P300/SSVEP
BCIs
have
only
recently
been
developed
and
validated,
and
very
few
avenues
to
improve
performance
have
been
explored.
New
method:
The
present
study
compares
an
established
hybrid
P300/SSVEP
BCIs
paradigm
to
a
new
paradigm
in
which
shape
changing,
instead
of
color
changing,
is
adopted
for
P300
evocation
to
decrease
the
degradation
on
SSVEP
strength.
Result:
The
result
shows
that
the
new
hybrid
paradigm
presented
in
this
paper
yields
much
better
performance
than
the
normal
hybrid
paradigm.
Comparison
with
existing
method:
A
performance
increase
of
nearly
20%
in
SSVEP
classification
is
achieved
using
the
new
hybrid
paradigm
in
comparison
with
the
normal
hybrid
paradigm.
All
the
paradigms
except
the
normal
hybrid
paradigm
used
in
this
paper
obtain
100%
accuracy
in
P300
classification.
Conclusions:
The
new
hybrid
P300/SSVEP
BCIs
paradigm
in
which
shape
changing,
instead
of
color
chang-
ing,
could
obtain
as
high
classification
accuracy
of
SSVEP
as
the
traditional
SSVEP
paradigm
and
could
obtain
as
high
classification
accuracy
of
P300
as
the
traditional
P300
paradigm.
P300
did
not
interfere
with
the
SSVEP
response
using
the
new
hybrid
paradigm
presented
in
this
paper,
which
was
superior
to
the
normal
hybrid
P300/SSVEP
paradigm.
©
2014
Elsevier
B.V.
All
rights
reserved.
1.
Introduction
A
brain–computer
interface
(BCI)
is
a
communication
system
that
allows
users
to
send
messages
or
commands
to
a
computer
or
other
external
device
through
direct
measures
of
brain
activity.
∗
Corresponding
author.
Tel.:
+86
2164253581;
fax:
+86
2164253386.
∗∗
Corresponding
author.
E-mail
addresses:
jinjingat@gmail.com,
jinjing@ecust.edu.cn
(J.
Jin),
xywang@ecust.edu.cn
(X.
Wang).
Hence,
BCIs
can
provide
communication
and
environmental
con-
trol
to
people
with
motor
disabilities,
as
well
as
healthy
people
when
other
means
of
communication
are
not
practical
(Wolpaw
et
al.,
2002).
Most
BCIs
rely
on
the
electroencephalogram
(EEG)
to
measure
brain
activity
in
real-time.
The
three
most
common
approaches
to
EEG-based
BCIs
are
named
after
the
types
of
EEG
phenomena
used
for
control,
namely
the
P300
visual
evoked
poten-
tial,
steady-state
visual
evoked
potential
(SSVEP),
and
event-related
(de)
synchronization
(ERD/ERS)
(Vidal,
1973;
Cheng
et
al.,
2002;
Pfurtscheller
and
Neuper,
2001;
Sellers
and
Donchin,
2006;
Jin
et
al.,
2011a;
Zhang
et
al.,
2012;
Wang
et
al.,
2006).
http://dx.doi.org/10.1016/j.jneumeth.2014.06.003
0165-0270/©
2014
Elsevier
B.V.
All
rights
reserved.
下载后可阅读完整内容，剩余9页未读，立即下载
2023-06-03 上传
2023-06-03 上传
2023-09-03 上传
2021-09-24 上传
2021-05-26 上传
2020-10-17 上传
2018-12-11 上传
2021-09-28 上传
weixin_38614417
- 粉丝: 5
- 资源: 915
我的内容管理
展开
最新资源
- 深入浅出：自定义 Grunt 任务的实践指南
- 网络物理突变工具的多点路径规划实现与分析
- multifeed: 实现多作者间的超核心共享与同步技术
- C++商品交易系统实习项目详细要求
- macOS系统Python模块whl包安装教程
- 掌握fullstackJS：构建React框架与快速开发应用
- React-Purify: 实现React组件纯净方法的工具介绍
- deck.js：构建现代HTML演示的JavaScript库
- nunn：现代C++17实现的机器学习库开源项目
- Python安装包 Acquisition-4.12-cp35-cp35m-win_amd64.whl.zip 使用说明
- Amaranthus-tuberculatus基因组分析脚本集
- Ubuntu 12.04下Realtek RTL8821AE驱动的向后移植指南
- 掌握Jest环境下的最新jsdom功能
- CAGI Toolkit：开源Asterisk PBX的AGI应用开发
- MyDropDemo: 体验QGraphicsView的拖放功能
- 远程FPGA平台上的Quartus II17.1 LCD色块闪烁现象解析
