1
Chapter 1
Introductory Chapter: Methods
and Applications of Neural Signal
Processing
Dingyi Pei and Ramana Vinjamuri
1. Introduction
Analytical methods are crucial to advance the field of brain sciences, and
efficient and effective methods of data analysis are required. Early from the last
century, the neural signals have been used in the engineering sphere to discover
mechanisms by which neural activity is generated and corresponding behavior
is produced. The function of the neural system was detected and studied using
engineering methodologies, and meanwhile, the engineering methodologies
helped to understand, repair, replace, enhance, or otherwise use the properties
and functions of neural systems. The neural signals are recorded by advanced
neural recording technologies, and the information is extracted to be used for
the understanding of neural representations of behavior. The external devices
are designed to assist signal acquisition, signal processing, or provide neural
feedback to humans.
Since movement is an essential activity of daily life, some of the major
applications of neural engineering in the field of motor control typically involve
motor function compensation, movement restoration, rehabilitation, disorder
detection, etc. A movement process is integrated and translated from the higher
levels of the control system, and it involves a series of transmissions to multistructure musculoskeletal coordination. The central nervous system (CNS)
works as a computational controller structure in motor behavior characterization and reorganization [1]. Multiple structures in the brain contribute to motor
control by connecting, integrating, and coordinating the motor-related information. Each structure is utilized in formulating a motor command when a particular action is performed, and the CNS switches the command between multiple
motor-related structures [2]. The mechanism of coordination and cooperation
of these structures in the brain could be determined as “black box” models,
providing the neural representations of relationships between motor command
input and predicted behavior output. These models may represent multiple brain
structures, especially the regions with synaptic plasticity that can receive and
send out information.
2. Neural recording and stimulation
Populations of neurons exhibit time-varying fluctuations in their aggregate
activity. Currently, various invasive or noninvasive recordings exist that can record
large amounts of spatial and temporal information from the human and nonhuman
Chapter 1
Introductory Chapter: Methods
and Applications of Neural Signal
Processing
Dingyi Pei and Ramana Vinjamuri
1. Introduction
Analytical methods are crucial to advance the field of brain sciences, and
efficient and effective methods of data analysis are required. Early from the last
century, the neural signals have been used in the engineering sphere to discover
mechanisms by which neural activity is generated and corresponding behavior
is produced. The function of the neural system was detected and studied using
engineering methodologies, and meanwhile, the engineering methodologies
helped to understand, repair, replace, enhance, or otherwise use the properties
and functions of neural systems. The neural signals are recorded by advanced
neural recording technologies, and the information is extracted to be used for
the understanding of neural representations of behavior. The external devices
are designed to assist signal acquisition, signal processing, or provide neural
feedback to humans.
Since movement is an essential activity of daily life, some of the major
applications of neural engineering in the field of motor control typically involve
motor function compensation, movement restoration, rehabilitation, disorder
detection, etc. A movement process is integrated and translated from the higher
levels of the control system, and it involves a series of transmissions to multistructure musculoskeletal coordination. The central nervous system (CNS)
works as a computational controller structure in motor behavior characterization and reorganization [1]. Multiple structures in the brain contribute to motor
control by connecting, integrating, and coordinating the motor-related information. Each structure is utilized in formulating a motor command when a particular action is performed, and the CNS switches the command between multiple
motor-related structures [2]. The mechanism of coordination and cooperation
of these structures in the brain could be determined as “black box” models,
providing the neural representations of relationships between motor command
input and predicted behavior output. These models may represent multiple brain
structures, especially the regions with synaptic plasticity that can receive and
send out information.
2. Neural recording and stimulation
Populations of neurons exhibit time-varying fluctuations in their aggregate
activity. Currently, various invasive or noninvasive recordings exist that can record
large amounts of spatial and temporal information from the human and nonhuman